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Keel bone damage (KBD) in laying hens is an important welfare problem in both conventional and organic egg production systems. We aimed to identify possible risk factors for KBD in organic hens by analysing cross-sectional data of 107 flocks assessed in eight European countries. Due to partly missing data, the final multiple regression model was based on data from 50 flocks. Keel bone damage included fractures and/or deviations, and was recorded, alongside with other animal based measures, by palpation and visual inspection of at least 50 randomly collected hens per flock between 52 and 73 weeks of age. Management and housing data were obtained by interviews, inspection and by feed analysis. Keel bone damage flock prevalences ranged from 3% to 88%. Compiled on the basis of literature and practical experience, 26 potential associative factors of KBD went into an univariable selection by Spearman correlation analysis or Mann–Whitney U test (with P<0.1 level). The resulting nine factors were presented to stepwise forward linear regression modelling. Aviary v. floor systems, absence of natural daylight in the hen house, a higher proportion of underweight birds, as well as a higher laying performance were found to be significantly associated with a higher percentage of hens with KBD. The final model explained 32% of the variation in KBD between farms. The moderate explanatory value of the model underlines the multifactorial nature of KBD. Based on the results increased attention should be paid to an adequate housing design and lighting that allows the birds easy orientation and safe manoeuvring in the system. Furthermore, feeding management should aim at sufficient bird live weights that fulfil breeder weight standards. In order to achieve a better understanding of the relationships between laying performance, feed management and KBD further investigations are needed.
Forecasting of glacier mass balance is important for optimal management of hydrological resources, especially where glacial meltwater constitutes a significant portion of stream flow, as is the case for many rivers in Iceland. In this study, a method was developed and applied to forecast the summer mass balance of Brúarjökull glacier in southeast Iceland. In the present study, many variables measured in the basin were evaluated, including glaciological snow accumulation data, various climate indices and meteorological measurements including temperature, humidity and radiation. The most relevant single predictor variables were selected using correlation analysis. The selected variables were used to define a set of potential multivariate linear regression models that were optimized by selecting an ensemble of plausible models showing good fit to calibration data. A mass-balance estimate was calculated as a uniform average across ensemble predictions. The method was evaluated using fivefold cross-validation and the statistical metrics Nash–Sutcliffe efficiency, the ratio of the root mean square error to the std dev. and percent bias. The results showed that the model produces satisfactory predictions when forced with initial condition data available at the beginning of the summer melt season, between 15 June and 1 July, whereas less reliable predictions are produced for longer lead times.
This study investigated the effects of rider weight in the BW ratio (BWR) range common for Icelandic horses (20% to 35%), on stride parameters in tölt in Icelandic horses. The kinematics of eight experienced Icelandic school horses were measured during an incremental exercise test using a high-speed camera (300 frames/s). Each horse performed five phases (642 m each) in tölt at a BWR between rider (including saddle) and horse starting at 20% (BWR20) and increasing to 25% (BWR25), 30% (BWR30), 35% (BWR35) and finally 20% (BWR20b) was repeated. One professional rider rode all horses and weight (lead) was added to saddle and rider as needed. For each phase, eight strides at speed of 5.5 m/s were analyzed for stride duration, stride frequency, stride length, duty factor (DF), lateral advanced placement, lateral advanced liftoff, unipedal support (UPS), bipedal support (BPS) and height of front leg action. Stride length became shorter (Y=2.73−0.004x; P<0.01) and more frequent (Y=2.56+0.002x; P<0.001) with added weight. Duty factor and BPS increased with increased BWR (P<0.001), whereas UPS decreased (P<0.001). Lateral advanced timing of limb placement and liftoff and height of front leg action were not affected by BWR (P>0.05). In conclusion, increased BWR decreased stride length and increased DF proportionally to the same extent in all limbs, whereas BPS increased at the expense of decreased UPS. These changes can be expected to decrease tölt quality when subjectively evaluated according to the breeding goals for the Icelandic horse. However, beat, symmetry and height of front leg lifting were not affected by BWR.
This study examined the effect of increasing BW ratio (BWR) between rider and horse, in the BWR range common for Icelandic horses (20% to 35%), on heart rate (HR), plasma lactate concentration (Lac), BWR at Lac 4 mmol/l (W4), breathing frequency (BF), rectal temperature (RT) and hematocrit (Hct) in Icelandic horses. In total, eight experienced school-horses were used in an incremental exercise test performed outdoors on an oval riding track and one rider rode all horses. The exercise test consisted of five phases (each 642 m) in tölt, a four-beat symmetrical gait, at a speed of 5.4±0.1 m/s (mean±SD), where BWR between rider (including saddle) and horse started at 20% (BWR20), was increased to 25% (BWR25), 30% (BWR30), and 35% (BWR35) and finally decreased to 20% (BWR20b). Between phases, the horses were stopped (~5.5 min) to add lead weights to specially adjusted saddle bags and a vest on the rider. Heart rate was measured during warm-up, the exercise test and after 5, 15 and 30 min of recovery and blood samples were taken and BF recorded at rest, and at end of each of these aforementioned occasions. Rectal temperature was measured at rest, at end of the exercise test and after a 30-min recovery period. Body size and body condition score (BCS) were registered and a clinical examination performed on the day before the test and for 2 days after. Heart rate and BF increased linearly (P<0.05) and Lac exponentially (P<0.05) with increasing BWR. The W4 was 22.7±4.3% (individual range 17.0% to 27.5%). There was a positive correlation between back BCS and W4 (r=0.75; P=0.032), but no other correlations between body measurements and W4 were found. Hematocrit was not affected by BWR (P>0.05), but negative correlations (P<0.05) existed between body size measurements and Hct. While HR, Hct and BF recovered to values at rest within 30 min, Lac and RT did not. All horses had no clinical remarks on palpation and at walk 1 and 2 days after the test. In conclusion, increasing BWR from 20% to 35% resulted in increased HR, Lac, RT and BF responses in the test group of experienced adult Icelandic riding horses. The horses mainly worked aerobically until BWR reached 22.7%, but considerable individual differences (17.0% to 27.5%) existed that were not linked to horse size, but to back BCS.
Older persons with alcohol problems have today become an all too common part of everyday elder care, but research in this area is still scarce. This article has a Swedish context with the aim of describing and analysing home care workers’ narratives about older people who can be characterised as heavy drinkers, i.e. people with severe alcohol problems who need considerable care for extended periods. Limited knowledge is available concerning this age group. This article therefore fills a knowledge gap about home care workers’ perspective about body work and the abject, and breaches the myth that older individuals should be able to drink as they prefer and/or notions of drinking alcohol as a last enjoyment in life. The care workers talked about how they got drawn into the daily lives of the care recipients and how they ended up in situations where they, on the one hand, removed the consequences of drinking, and on the other, felt that they sustained the drinking by cleaning out dirt and washing the care recipients’ bodies.
Precipitation of amorphous silica (SiO2) in geothermal power plants, although a common factor limiting the efficiency of geothermal energy production, is poorly understood and no universally applicable mitigation strategy to prevent or reduce precipitation is available. This is primarily due to the lack of understanding of the precipitation mechanism of amorphous silica in geothermal systems.
In the present study data are presented about microstructures and compositions of precipitates formed on scaling plates inserted at five different locations in the pipelines at the Hellisheiði power station (SW-Iceland). Precipitates on these plates formed over 6 to 8 weeks of immersion in hot (120 or 60ºC), fast-flowing and silica-supersaturated geothermal fluids (~800 ppm of SiO2). Although the composition of the precipitates is fairly homogeneous, with silica being the dominant component and Fe sulfides as a less common phase, the microstructures of the precipitates are highly variable and dependent on the location within the geothermal pipelines. The silica precipitates have grown through aggregation and precipitation of silica particles that precipitated homogeneously in the geothermal fluid. Five main factors were identified that may control the precipitation of silica: (1) temperature, (2) fluid composition, (3) fluid-flow regime, (4) distance along the flow path, and (5) immersion time.
On all scaling plates, a corrosion layer was found underlying the silica precipitates indicating that, once formed, the presence of a silica layer probably protects the steel pipe surface against further corrosion. Yet silica precipitates influence the flow of the geothermal fluids and therefore can limit the efficiency of geothermal power stations.
This study examined the response in terms of heart rate (HR), respiratory rate (RR), haematocrit (Htc), rectal temperature (RT), and some plasma variables in Icelandic horses of different sexes and ages performing the riding assessment in a breed evaluation field test (BEFT). The study was conducted in Iceland on 266 horses (180 mares and 86 stallions, divided into four age groups; 4, 5, 6 and ⩾7 years old). RT and RR were recorded and blood samples were taken before the warm-up and after the riding assessment. Horse HR, velocity and distance were recorded during the warm-up, the riding assessment and a 5-min recovery period. The distance covered in the BEFT was 2.9±0.4 km (range: 1.8 to 3.8 km, n=248), the duration was 9:37±1:22 min:s (range: 5:07 to 15:32 min:s, n=260) and the average speed was 17.8±1.4 km/h (range: 13.2 to 21.3 km/h, n=248). Average HR was 184±13 b.p.m. (range: 138 to 210 b.p.m., n=102) and peak HR 224±9 b.p.m. (range: 195 to 238 b.p.m., n=102), and 36% of the BEFT was performed at HR ⩾200 b.p.m. Post-exercise plasma lactate concentration (Lac) was 18.0±6.5 mmol/l (range: 2.1 to 34.4 mmol/l, n=266), and there was an increase in total plasma protein, plasma creatine kinase and aspartate amino transferase concentration, as well as RR, RT and Htc. Stallions covered a longer total distance (in the warm-up and BEFT) (P<0.05), at a faster speed during BEFT (P<0.001) than mares and had higher Htc and lower HR and post-exercise Lac values. There were few effects of age, but the 4- and 5-year-old horses had lower Htc than older horses and 4-year-old horses had higher post-exercise RR than older horses, although they were ridden for a shorter distance, shorter duration and at lower peak velocity (P<0.1). The results showed that the riding assessment in the BEFT is a high-intensity exercise. The results also showed that aerobic fitness was higher in stallions and that age had a limited effect on the physiological response. It is suggested that these results should be used as a guide for the development of training programmes and fitness tests in Icelandic horses that would improve both performance and welfare of the horse.
Heterogeneous cellular network (HCN) deployments may imply an order of magnitude more network nodes than conventional homogenous macrocell deployments. Therefore, it is important that the integration and operation of these new nodes require minimal manual efforts from operators. Self-organizing network (SON) features can be seen as essential enablers to facilitate service as well as network deployment and management. The main objectives of SONs are to reduce the deployment costs, simplify network management (managing a plethora of radio access technologies (RATs) without significantly increasing operational expenses) and enhance network performance.
Within the 3rd Generation Partnership Project (3GPP) Long Term Evolution (LTE), SON was among the early system requirements, and SON features were already included in the first 3GPPLTErelease, i.e., Release 8 . SONwork items in 3GPP [2, 3] have been inspired by the SONstudies and the set of requirements defined by the operators' alliance Next Generation Mobile Networks (NGMN) . This chapter addresses HCN aspects of SON, although these automation features are applicable to other types of network deployment aswell. Themain focus is on LTE, but Universal Mobile Telecommunication System (UMTS) and multiple RATs will also be considered where applicable. More general discussions about SON can be found in [5–9], and a discussion with special focus on femtocells can be found in .
SON operations are supported by the operation, administration, and maintenance (OAM) architecture, which is presented in Section 6.2, and are commonly divided into four key components/phases: planning, self-configuration, self-optimization and selfhealing, as illustrated in Fig. 6.1.
This detailed, up-to-date introduction to heterogeneous cellular networking introduces its characteristic features, the technology underpinning it and the issues surrounding its use. Comprehensive and in-depth coverage of core topics catalogue the most advanced, innovative technologies used in designing and deploying heterogeneous cellular networks, including system-level simulation and evaluation, self-organisation, range expansion, cooperative relaying, network MIMO, network coding and cognitive radio. Practical design considerations and engineering tradeoffs are also discussed in detail, including handover management, energy efficiency and interference management techniques. A range of real-world case studies, provided by industrial partners, illustrate the latest trends in heterogeneous cellular networks development. Written by leading figures from industry and academia, this is an invaluable resource for all researchers and practitioners working in the field of mobile communications.
My name is Gordon Mansfield, and I currently serve as the elected chairman of the Small Cell Forum. The Forum is an industry body that promotes and drives the wide-scale adoption of small cell technologies to improve coverage, capacity and services delivered by mobile networks. I have many years of experience in the space, having previously served on the Femto Forum board from 2008-2010 and having led a tier one operators small cell effort since 2007. I consider it a great honor to be asked to write the foreword for this very informative book on small cells and heterogeneous networks. The authors are all highly respected researchers in academia and in industry, who have spent years working on the topics covered.
In recent years, small cells have become a very big topic when discussing mobile Internet and the tremendous data growth experienced over the past five years by operators around the globe. When we look at the recent history of data growth, some operators have experienced a 20,000 percent growth in data from 2007-2011. Combine that with the incredible forecast coming from all parts of the industry suggesting 10X and higher growth over the next four to five years, and it becomes clear that new ways to serve this data growth are necessary. We cannot continue to rely on new spectrum and advances in the air interface alone to sustain these types of data growth.
Driven by a new generation of wireless user equipments and the proliferation of bandwidth-intensive applications, mobile data traffic and network load are increasing in unexpected ways, and are straining current cellular networks to a breaking point. In this context, heterogeneous cellular networks, which are characterized by a large number of network nodes with different transmit power levels and radio frequency coverage areas, including macrocells, remote radio heads, microcells, picocells, femtocells and relay nodes, have attracted much momentum in the wireless industry and research community, and have also gained the attention of standardization bodies such as the 3rd Generation Partnership Project (3GPP) LTE/LTE-Advanced and the Institute of Electrical and Electronics Engineers (IEEE) Mobile Worldwide Interoperability for Microwave Access (WiMAX).
The impending worldwide deployments of heterogeneous cellular networks bring about not only opportunities but also challenges. Major technical challenges include the co-existence of various neighboring and/or overlapping cells, intercell interference and mobility management, backhaul provisioning, and self-organization that is crucial for efficient roll-outs of user-deployed low-power nodes. These challenges need to be addressed urgently to make the best out of heterogeneous cellular networks. This asks for a thorough revisit of contemporary wireless network technologies, such as network architecture and protocol designs, spectrum allocation strategies, call management mechanisms, etc. There is also an urgent need in the wireless industry, academia and even end-users to better understand the technical details and performance gains that heterogeneous cellular networks would make possible.
As discussed in Chapter 1, heterogeneous cellular networks (HCNs) with low-power nodes (LPNs) are important for improving the capacity and coverage of next generation broadband wireless communication systems. However, interference problems in HCNs pose an important challenge, and thus efficient interference management techniques are required to fully benefit from their deployments. The main contribution of this chapter is to review interference problems and interference management techniques for HCNs. A general notion of macrocell base stations (MBSs) and LPNs is adopted, but the simulations are based on Long Term Evolution (LTE) scenarios with macro eNBs and pico eNBs or femto HeNBs. More specifically, cell-selection and interference coordination methods are discussed, including mechanisms recently proposed in the 3rd Generation Partnership Project (3GPP) LTE, and their performances are evaluated through system-level simulations. In such simulations, LTE-specific notation is used, and macrocell user equipment (MUE) and picocell user equipment (PUE) denotes UEs served by macro eNBs and pico eNBs, respectively.
This chapter is organized as follows. First, Section 7.1 reviews the main reasons for excessive intercell interference in HCNs. In Section 7.2, due to its significance, range expansion (RE) for HCNs is treated in more detail. Some example simulation results that demonstrate the downlink (DL)/uplink (UL) coverage imbalance in heterogeneous deployments are also provided. Section 7.3 gives a high-level overview of intercell interference coordination (ICIC) methods that are applicable to HCNs, and the next three sections are dedicated to specific ICIC approaches: frequency-domain, power-based, and time-domain ICIC techniques are discussed in Section 7.4, Section 7.5, and Section 7.6, respectively.