A Model for Estimating Network Infrastructure Costs: A Case for All-Fibre Networks

The 21st century is an era that has been characterised by phenomenal growth in data rates at the local area network (intranet), extranet and the Internet, a trend pushed by deployment of “bandwidth hungry” applications such VoIP, security surveillance systems, video conferencing and streaming of online multimedia content. Due to demand placed on network resources by these applications physical layer cabling solutions have had to evolve to support faster, improved LAN technologies such as Gigabit Ethernet. Although new network architectures (such as Centralised Fibre networks) address current and long term demands of the modern networking environment, concerns have been raised about its cost viability. The key problem identified in this study was an inadequacy of suitable tools that aid decision making when estimating the cost of a network infrastructure project. Factors of importance in this regard were collected in a survey and used in development of a cost model. A network was designed based on two architectures – centralised fibre (all-fibre network) and hierarchical star (UTP for horizontal cabling and optical fibre for backbone cabling). Thereafter, cost of implementing these two architectures was calculated using the model. Based on the results computed from the cost model, the all-fibre network (centralised fibre architecture) was found to be more cost effective than the hierarchical star network. Keywords: centralised fibre architecture, hierarchical star architecture, structured cabling, multimode optical fibre, singlemode optical fibre, backbon

Antimicrobial resistance among migrants in Europe: a systematic review and meta-analysis

BACKGROUND: Rates of antimicrobial resistance (AMR) are rising globally and there is concern that increased migration is contributing to the burden of antibiotic resistance in Europe. However, the effect of migration on the burden of AMR in Europe has not yet been comprehensively examined. Therefore, we did a systematic review and meta-analysis to identify and synthesise data for AMR carriage or infection in migrants to Europe to examine differences in patterns of AMR across migrant groups and in different settings. METHODS: For this systematic review and meta-analysis, we searched MEDLINE, Embase, PubMed, and Scopus with no language restrictions from Jan 1, 2000, to Jan 18, 2017, for primary data from observational studies reporting antibacterial resistance in common bacterial pathogens among migrants to 21 European Union-15 and European Economic Area countries. To be eligible for inclusion, studies had to report data on carriage or infection with laboratory-confirmed antibiotic-resistant organisms in migrant populations. We extracted data from eligible studies and assessed quality using piloted, standardised forms. We did not examine drug resistance in tuberculosis and excluded articles solely reporting on this parameter. We also excluded articles in which migrant status was determined by ethnicity, country of birth of participants' parents, or was not defined, and articles in which data were not disaggregated by migrant status. Outcomes were carriage of or infection with antibiotic-resistant organisms. We used random-effects models to calculate the pooled prevalence of each outcome. The study protocol is registered with PROSPERO, number CRD42016043681. FINDINGS: We identified 2274 articles, of which 23 observational studies reporting on antibiotic resistance in 2319 migrants were included. The pooled prevalence of any AMR carriage or AMR infection in migrants was 25·4% (95% CI 19·1-31·8; I2 =98%), including meticillin-resistant Staphylococcus aureus (7·8%, 4·8-10·7; I2 =92%) and antibiotic-resistant Gram-negative bacteria (27·2%, 17·6-36·8; I2 =94%). The pooled prevalence of any AMR carriage or infection was higher in refugees and asylum seekers (33·0%, 18·3-47·6; I2 =98%) than in other migrant groups (6·6%, 1·8-11·3; I2 =92%). The pooled prevalence of antibiotic-resistant organisms was slightly higher in high-migrant community settings (33·1%, 11·1-55·1; I2 =96%) than in migrants in hospitals (24·3%, 16·1-32·6; I2 =98%). We did not find evidence of high rates of transmission of AMR from migrant to host populations. INTERPRETATION: Migrants are exposed to conditions favouring the emergence of drug resistance during transit and in host countries in Europe. Increased antibiotic resistance among refugees and asylum seekers and in high-migrant community settings (such as refugee camps and detention facilities) highlights the need for improved living conditions, access to health care, and initiatives to facilitate detection of and appropriate high-quality treatment for antibiotic-resistant infections during transit and in host countries. Protocols for the prevention and control of infection and for antibiotic surveillance need to be integrated in all aspects of health care, which should be accessible for all migrant groups, and should target determinants of AMR before, during, and after migration. FUNDING: UK National Institute for Health Research Imperial Biomedical Research Centre, Imperial College Healthcare Charity, the Wellcome Trust, and UK National Institute for Health Research Health Protection Research Unit in Healthcare-associated Infections and Antimictobial Resistance at Imperial College London

Multiple Nuclear Gene Phylogenetic Analysis of the Evolution of Dioecy and Sex Chromosomes in the Genus Silene

In the plant genus Silene, separate sexes and sex chromosomes are believed to have evolved twice. Silene species that are wholly or largely hermaphroditic are assumed to represent the ancestral state from which dioecy evolved. This assumption is important for choice of outgroup species for inferring the genetic and chromosomal changes involved in the evolution of dioecy, but is mainly based on data from a single locus (ITS). To establish the order of events more clearly, and inform outgroup choice, we therefore carried out (i) multi-nuclear-gene phylogenetic analyses of 14 Silene species (including 7 hermaphrodite or gynodioecious species), representing species from both Silene clades with dioecious members, plus a more distantly related outgroup, and (ii) a BayesTraits character analysis of the evolution of dioecy. We confirm two origins of dioecy within this genus in agreement with recent work on comparing sex chromosomes from both clades with dioecious species. We conclude that sex chromosomes evolved after the origin of Silene and within a clade that includes only S. latifolia and its closest relatives. We estimate that sex chromosomes emerged soon after the split with the ancestor of S. viscosa, the probable closest non-dioecious S. latifolia relative among the species included in our study

Multimodal pyrethroid resistance in malaria vectors, Anopheles gambiae s.s., Anopheles arabiensis, and Anopheles funestus s.s. in western Kenya.

Anopheles gambiae s.s., Anopheles arabiensis, and Anopheles funestus s.s. are the most important species for malaria transmission. Pyrethroid resistance of these vector mosquitoes is one of the main obstacles against effective vector control. The objective of the present study was to monitor the pyrethroid susceptibility in the 3 major malaria vectors in a highly malaria endemic area in western Kenya and to elucidate the mechanisms of pyrethroid resistance in these species. Gembe East and West, Mbita Division, and 4 main western islands in the Suba district of the Nyanza province in western Kenya were used as the study area. Larval and adult collection and bioassay were conducted, as well as the detection of point mutation in the voltage-gated sodium channel (1014L) by using direct DNA sequencing. A high level of pyrethroid resistance caused by the high frequency of point mutations (L1014S) was detected in An. gambiae s.s. In contrast, P450-related pyrethroid resistance seemed to be widespread in both An. arabiensis and An. funestus s.s. Not a single L1014S mutation was detected in these 2 species. A lack of cross-resistance between DDT and permethrin was also found in An. arabiensis and An. funestus s.s., while An. gambiae s.s. was resistant to both insecticides. It is noteworthy that the above species in the same area are found to be resistant to pyrethroids by their unique resistance mechanisms. Furthermore, it is interesting that 2 different resistance mechanisms have developed in the 2 sibling species in the same area individually. The cross resistance between permethrin and DDT in An. gambiae s.s. may be attributed to the high frequency of kdr mutation, which might be selected by the frequent exposure to ITNs. Similarly, the metabolic pyrethroid resistance in An. arabiensis and An. funestus s.s. is thought to develop without strong selection by DDT

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

Internet of Things Based Model For Identifying Pediatric Emergency Cases

Pediatric emergency cases need rapid systems that measure vital body parameters data, analyze and categorize emergency cases for precise action. Current systems use manual examination resulting in delayed medication, death, or other severe medical conditions.In this paper, we propose a Internet of Things (IoT) based model, created using Balena fin with Raspberry pi compute module. It is used for determining emergency cases, in pediatric section, specifically the triage section. It is later tested using hospital data that represents the vital parameters in pediatric. Our approach entails designing and setting up the hardware and software infrastructure, to accommodate data via Bluetooth protocol, and transmit it to the cloud server database via Message Queuing Telemetry Transport (MQTT). Later, we perform machine learning on the data by training a model and finally develop a Plotly Dash analytical application integrating the model for visualization near real-time.Findings show that emergency cases are detected using vital body parameters which include the body temperature, oxygen levels, heart rate and the age. The model indicates a 97% accuracy.In conclusion, children’s emergency cases are detected in time using IoT gadgets and machine learning classification

Design and Implementation of Resilient Cooperative Bait Detection Technique to Curb Cooperative Black Hole Attacks in MANETs Using DSR Protocol

Mobile Ad-hoc networks (MANETs) are unique type of wireless networks that are infrastructureless and with no centralised management. Nodes in MANETs act as both routers and hosts. The nodes are free to join and leave the network. Routes are established by use of special routing protocols. Mobility of nodes makes the network topology constantly dynamic. The unique characteristics of MANETs make their security a challenging endeavor. MANETs are prone to a range of security attacks such worm hole, Sybil, black hole, among others. Blackhole is a form of denial of service (DoS) attack. The black hole nodes work in association forming cooperative black hole attacks that drop or redirecting data packets. This compromises the communication process in mission critical areas. The paper proposes a Resilient Cooperative Bait Detection Technique (RCBDT) using DSR protocol to curb collaborative black hole attacks in MANETs. The proposed technique uses source node address as the bait address. Further, RCBDT uses an algorithm that checks nodes energy levels before engaging them in packet transmission. The technique was designed, implemented and simulated in Network Simulator Version 3(NS-3). The proposed technique was compared with Cooperative Bait Detection Scheme (CBDS) and Extended Cooperative Bait Detection Scheme (ECBDS). Simulation results indicate that the proposed technique is superior to benchmark techniques in terms of Packet Delivery Ratio (PDR), End-to-End Delay and Routing Overheads

Developing Hybrid-Based Recommender System with Naïve Bayes Optimization to Increase Prediction Efficiency

Commerce and entertainment world today have shifted to the digital platforms where customer preferences are suggested by recommender systems. Recommendations have been made using a variety of methods such as content-based, collaborative filtering-based or their hybrids. Collaborative systems are common recommenders, which use similar users’ preferences. They however have issues such as data sparsity, cold start problem and lack of scalability. When a small percentage of users express their preferences, data becomes highly sparse, thus affecting quality of recommendations. New users or items with no preferences, forms cold start issues affecting recommendations. High amount of sparse data affects how the user-item matrices are formed thus affecting the overall recommendation results. How to handle data input in the recommender engine while reducing data sparsity and increase its potential to scale up is proposed. This paper proposed development of hybrid model with data optimization using a Naïve Bayes classifier, with an aim of reducing data sparsity problem and a blend of collaborative filtering model and association rule mining-based ensembles, for recommending items with an aim of improving their predictions. Machine learning using python on Jupyter notebook was used to develop the hybrid. The models were tested using MovieLens 100k and 1M datasets. We demonstrate the final recommendations of the hybrid having new top ten highly rated movies with 68% approved recommendations. We confirm new items suggested to the active user(s) while less sparse data was input and an improved scaling up of collaborative filtering model, thus improving model efficacy and better predictions

Metrics For Evaluating Alerts in Intrusion Detection Systems

Network intrusions compromise the network’s confidentiality, integrity and availability of resources. Intrusion detection systems (IDSs) have been implemented to prevent the problem. Although IDS technologies are promising, their ability of detecting true alerts is far from being perfect. One problem is that of producing large numbers of false alerts, which are termed as malicious by the IDS. In this paper we propose a set of metrics for evaluating the IDS alerts. The metrics will identify false, low-level and redundant alerts by mapping alerts on a vulnerability database and calculating their impact. The metrics are calculated using a metric tool that we developed. We validated the metrics using Weyuker’s properties and Kaner’s framework. The metrics can be considered as mathematically valid since they satisfied seven of the nine Weyuker’s properties. In addition, they can be considered as workable since they satisfied all the evaluation questions from Kaner’s framework

Optimized Trust-Based DSR Protocol to Curb Cooperative Blackhole Attacks in MANETs Using NS-3

MANETs Communication relies on special routing protocols that make security a challenging endeavor. MANETs are open to a range of active and passive attacks; black hole attack is an active attack affects the network layer. Cooperative black hole attack is a form of denial of service attack comprised of more than one black hole nodes that collaborate in order to drop data packets during communication process. In our study, we used the concept of trust to extend the DSR protocol in order to mitigate cooperative black hole attacks that leads to loss of data packets. The paper proposes an Optimized Trust-Based Dynamic Source Routing protocol. The proposed protocol integrates dynamic trust and friendship functions in the standard DSR protocol. The proposed protocol was designed, implemented and simulated in Network Simulator version 3 (NS-3). Simulation results indicate that the proposed protocol is superior to standard Dynamic Source Routing (DSR) protocol and Ad hoc On Demand Vector (AODV) protocols used as the benchmark protocols; in terms of packet delivery ratio, routing overhead and end-to-end delays and throughput used as performance metrics. The Optimized Trust-Based DSR protocol had a packet delivery ratio of above 95%, routing overhead of about 4.75% and an end-to-end delay of between 0.9 seconds and 1.65 seconds and a throughput of 95.6 Kbps