The effect of feeding a high fibre diet on the welfare of sows housed in large dynamic groups

This study assessed the effect of increasing fibre levels in the concentrate ration on the welfare of sows housed in a large dynamic group. One hundred and twelve Large White x Landrace sows were allocated to one of two treatments over six replicates. Treatments were as follows: (i) High Fibre diet (similar to 15% CF [Crude Fibre]), and (ii) Control diet (similar to 5% CF). Treatments were applied to two separate dynamic groups each containing 33 (+/- 3) sows in a cross-over design, after three replicates the treatments were switched between the groups. Approximately nine sows were replaced in each of these groups at 3-week intervals (each replacement constituting a replicate of the study). Sows on the high fibre diet spent a greater percentage of time lying (High Fibre: 43.8, Control. 28.0, SEM 3.25%), while sows on the control diet spent more time sham chewing (High Fibre: 7.2, Control: 28.8, SEM 1.55%). Sows newly introduced to the group on the high fibre treatment spent proportionally more time in the kennel areas compared to newly introduced sows in the control treatment (High Fibre. 0.893, Control. 0 788, SEM 5 10) In general, aggression occurred at a very low frequency and overall levels did not differ between treatments (High Fibre: 0.005, Control: 0.003, SEM 0.0007 [occurrences per min)). However, sows in the control treatment performed head thrusting (High Fibre: 0.02, Control: 0.00, SEM 0.001 [occurrences per mini), and biting behaviour (High Fibre. 002, Control. 0.01, SEM 0.002 [occurrences per min]) more frequently than sows on the high fibre diet. There was no effect of treatment on physiological parameters such as plasma cortisol (High Fibre: 1.34, Control: 1.44, SEM 0.114 ng ml(-1)) or haptoglobin levels (High Fibre. 0.73, Control. 0.64, SEM 0.080 mg ml(-1)). In summary, provision of a high fibre diet had a positive effect on the welfare of group-housed dry sows Sows on the high fibre treatment spent more time resting in the kennel areas, less time performing stereotypic behaviours and showed a reduction in some aggressive behaviours relative to sows fed the control diet.

Influence of social status on the welfare of sows in static and dynamic groups

Forty-eight Large White x Landrace multiparous sows were mixed into twelve groups of four animals after their piglets were weaned. These groups were defined as static, with no animals being added to or removed from the groups after their formation. Aggressive and submissive behaviours were recorded continuously for 9 h after the sows were mixed, and the sows were assigned high or low social status on the basis of their relative aggressiveness and success in aggressive interactions. After five weeks, each static group was mixed into a dynamic group of 40+/-2 sows for an 11-week period. Three static groups (ie 12 animals) at a time were added to the dynamic group at three-week intervals; the same number of animals was removed at these time-points in order to maintain the group number at 40+/-2. Injury levels increased significantly with the transition from static groups to the dynamic group (P

Tri-axial accelerometers tease apart discrete behaviours in the common cuttlefish Sepia officinalis

Acceleration data loggers can be used to construct time-energy budgets or identify specific behaviours in free living animals. Within a marine context such devices have been largely deployed on vertebrates with comparatively little attention paid to commercially important invertebrates such as cephalopod molluscs. Here we tested the utility of tri-axial accelerometers to tease apart six discrete behaviours in the common cuttlefish Sepia officinalis. By considering depth profiles in conjunction with body pitch and roll and overall dynamic body acceleration we were able to make distinctions between resting at the seabed, active swimming, mating, post-coital panting and active manoeuvring along the seabed. © 2012 Marine Biological Association of the United Kingdom.

Developing management capability within a horizontal supply chain in performance measurement deployment and evolution: A Dynamic Capabilities and Goal Theory perspective

Complex collaboration in rapidly changing business environments create challenges for management capability in Utility Horizontal Supply Chains (UHSCs) involving the deploying and evolving of performance measures. The aim of the study is twofold. First, there is a need to explore how management capability can be developed and used to deploy and evolve Performance Measurement (PM), both across a UHSC and within its constituent organisations, drawing upon a theoretical nexus of Dynamic Capability (DC) theory and complementary Goal Theory. Second, to make a contribution to knowledge by empirically building theory using these constructs to show the management motivations and behaviours within PM-based DCs. The methodology uses an interpretive theory building, multiple case based approach (n=3) as part of a USHC. The data collection methods include, interviews (n=54), focus groups (n=10), document analysis and participant observation (reflective learning logs) over a five-year period giving longitudinal data. The empirical findings lead to the development of a conceptual framework showing that management capabilities in driving PM deployment and evolution can be represented as multilevel renewal and incremental Dynamic Capabilities, which can be further understood in terms of motivation and behaviour by Goal-Theoretic constructs. In addition three interrelated cross cutting themes of management capabilities in consensus building, goal setting and resource change were identified. These management capabilities require carefully planned development and nurturing within the UHSC. 

Collective intelligence on dynamic manufacturing scheduling optimization

Swarm Intelligence (SI) is a growing research field of Artificial Intelligence (AI). SI is the general term for several computational techniques which use ideas and get inspiration from the social behaviours of insects and of other animals. This paper presents hybridization and combination of different AI approaches, like Bio-Inspired Techniques (BIT), Multi-Agent systems (MAS) and Machine Learning Techniques (ML T). The resulting system is applied to the problem of jobs scheduling to machines on dynamic manufacturing environments.

Characterizing Dynamic Optimization Benchmarks for the Comparison of Multi-Modal Tracking Algorithms

Population-based metaheuristics, such as particle swarm optimization (PSO), have been employed to solve many real-world optimization problems. Although it is of- ten sufficient to find a single solution to these problems, there does exist those cases where identifying multiple, diverse solutions can be beneficial or even required. Some of these problems are further complicated by a change in their objective function over time. This type of optimization is referred to as dynamic, multi-modal optimization. Algorithms which exploit multiple optima in a search space are identified as niching algorithms. Although numerous dynamic, niching algorithms have been developed, their performance is often measured solely on their ability to find a single, global optimum. Furthermore, the comparisons often use synthetic benchmarks whose landscape characteristics are generally limited and unknown. This thesis provides a landscape analysis of the dynamic benchmark functions commonly developed for multi-modal optimization. The benchmark analysis results reveal that the mechanisms responsible for dynamism in the current dynamic bench- marks do not significantly affect landscape features, thus suggesting a lack of representation for problems whose landscape features vary over time. This analysis is used in a comparison of current niching algorithms to identify the effects that specific landscape features have on niching performance. Two performance metrics are proposed to measure both the scalability and accuracy of the niching algorithms. The algorithm comparison results demonstrate the algorithms best suited for a variety of dynamic environments. This comparison also examines each of the algorithms in terms of their niching behaviours and analyzing the range and trade-off between scalability and accuracy when tuning the algorithms respective parameters. These results contribute to the understanding of current niching techniques as well as the problem features that ultimately dictate their success.

A neural network enhanced generalized minimum variance self-tuning proportional, integral and derivative control algorithm for complex dynamic systems

A neural network enhanced proportional, integral and derivative (PID) controller is presented that combines the attributes of neural network learning with a generalized minimum-variance self-tuning control (STC) strategy. The neuro PID controller is structured with plant model identification and PID parameter tuning. The plants to be controlled are approximated by an equivalent model composed of a simple linear submodel to approximate plant dynamics around operating points, plus an error agent to accommodate the errors induced by linear submodel inaccuracy due to non-linearities and other complexities. A generalized recursive least-squares algorithm is used to identify the linear submodel, and a layered neural network is used to detect the error agent in which the weights are updated on the basis of the error between the plant output and the output from the linear submodel. The procedure for controller design is based on the equivalent model, and therefore the error agent is naturally functioned within the control law. In this way the controller can deal not only with a wide range of linear dynamic plants but also with those complex plants characterized by severe non-linearity, uncertainties and non-minimum phase behaviours. Two simulation studies are provided to demonstrate the effectiveness of the controller design procedure.

Dynamic distributed lanes: motion planning for multiple autonomous vehicles

Unorganized traffic is a generalized form of travel wherein vehicles do not adhere to any predefined lanes and can travel in-between lanes. Such travel is visible in a number of countries e.g. India, wherein it enables a higher traffic bandwidth, more overtaking and more efficient travel. These advantages are visible when the vehicles vary considerably in size and speed, in the absence of which the predefined lanes are near-optimal. Motion planning for multiple autonomous vehicles in unorganized traffic deals with deciding on the manner in which every vehicle travels, ensuring no collision either with each other or with static obstacles. In this paper the notion of predefined lanes is generalized to model unorganized travel for the purpose of planning vehicles travel. A uniform cost search is used for finding the optimal motion strategy of a vehicle, amidst the known travel plans of the other vehicles. The aim is to maximize the separation between the vehicles and static obstacles. The search is responsible for defining an optimal lane distribution among vehicles in the planning scenario. Clothoid curves are used for maintaining a lane or changing lanes. Experiments are performed by simulation over a set of challenging scenarios with a complex grid of obstacles. Additionally behaviours of overtaking, waiting for a vehicle to cross and following another vehicle are exhibited.

A preliminary study into emergent behaviours in a lattice of interacting nonlinear resonators and oscillators

Future sensor arrays will be composed of interacting nonlinear components with complex behaviours with no known analytic solutions. This paper provides a preliminary insight into the expected behaviour through numerical and analytical analysis. Specically, the complex behaviour of a periodically driven nonlinear Duffing resonator coupled elastically to a van der Pol oscillator is investigated as a building block in a 2D lattice of such units with local connectivity. An analytic treatment of the 2-device unit is provided through a two-time-scales approach and the stability of the complex dynamic motion is analysed. The pattern formation characteristics of a 2D lattice composed of these units coupled together through nearest neighbour interactions is analysed numerically for parameters appropriate to a physical realisation through MEMS devices. The emergent patterns of global and cluster synchronisation are investigated with respect to system parameters and lattice size.

Metallocene ethylene-1-octene copolymers:influence of comonomer content on thermo-mechanical, rheological, and thermo-oxidative behaviours before and after melt processing in an internal mixer

The influence of the comonomer content in a series of metallocene-based ethylene-1-octene copolymers (m-LLDPE) on thermo-mechanical, rheological, and thermo-oxidative behaviours during melt processing were examined using a range of characterisation techniques. The amount of branching was calculated from 13C NMR and studies using differential scanning calorimetry (DSC) and dynamic mechanical analysis (DMA) were employed to determine the effect of short chain branching (SCB, comonomer content) on thermal and mechanical characteristics of the polymer. The effect of melt processing at different temperatures on the thermo-oxidative behaviour of the polymers was investigated by examining the changes in rheological properties, using both melt flow and capillary rheometry, and the evolution of oxidation products during processing using infrared spectroscopy. The results show that the comonomer content and catalyst type greatly affect thermal, mechanical and oxidative behaviour of the polymers. For the metallocene polymer series, it was shown from both DSC and DMA that (i) crystallinity and melting temperatures decreased linearly with comonomer content, (ii) the intensity of the ß-transition increased, and (iii) the position of the tan δmax peak corresponding to the a-transition shifted to lower temperatures, with higher comonomer content. In contrast, a corresponding Ziegler polymer containing the same level of SCB as in one of the m-LLDPE polymers, showed different characteristics due to its more heterogeneous nature: higher elongational viscosity, and a double melting peak with broader intensity that occurred at higher temperature (from DSC endotherm) indicating a much broader short chain branch distribution. The thermo-oxidative behaviour of the polymers after melt processing was similarly influenced by the comonomer content. Rheological characteristics and changes in concentrations of carbonyl and the different unsaturated groups, particularly vinyl, vinylidene and trans-vinylene, during processing of m-LLDPE polymers, showed that polymers with lower levels of SCB gave rise to predominantly crosslinking reactions at all processing temperatures. By contrast, chain scission reactions at higher processing temperatures became more favoured in the higher comonomer-containing polymers. Compared to its metallocene analogue, the Ziegler polymer showed a much higher degree of crosslinking at all temperatures because of the high levels of vinyl unsaturation initially present.

Dynalog: an automated dynamic analysis framework for characterizing android applications

Android is becoming ubiquitous and currently has the largest share of the mobile OS market with billions of application downloads from the official app market. It has also become the platform most targeted by mobile malware that are becoming more sophisticated to evade state-of-the-art detection approaches. Many Android malware families employ obfuscation techniques in order to avoid detection and this may defeat static analysis based approaches. Dynamic analysis on the other hand may be used to overcome this limitation. Hence in this paper we propose DynaLog, a dynamic analysis based framework for characterizing Android applications. The framework provides the capability to analyse the behaviour of applications based on an extensive number of dynamic features. It provides an automated platform for mass analysis and characterization of apps that is useful for quickly identifying and isolating malicious applications. The DynaLog framework leverages existing open source tools to extract and log high level behaviours, API calls, and critical events that can be used to explore the characteristics of an application, thus providing an extensible dynamic analysis platform for detecting Android malware. DynaLog is evaluated using real malware samples and clean applications demonstrating its capabilities for effective analysis and detection of malicious applications.

A Video Analysis of Intra- and Inter-professional Leadership Behaviours Within “The Burns Suite”: Identifying Key Leadership Models

Objective Leadership is particularly important in complex highly interprofessional health care contexts involving a number of staff, some from the same specialty (intraprofessional), and others from different specialties (interprofessional). The authors recently published the concept of “The Burns Suite” (TBS) as a novel simulation tool to deliver interprofessional and teamwork training. It is unclear which leadership behaviors are the most important in an interprofessional burns resuscitation scenario, and whether they can be modeled on to current leadership theory. The purpose of this study was to perform a comprehensive video analysis of leadership behaviors within TBS. Methods A total of 3 burns resuscitation simulations within TBS were recorded. The video analysis was grounded-theory inspired. Using predefined criteria, actions/interactions deemed as leadership behaviors were identified. Using an inductive iterative process, 8 main leadership behaviors were identified. Cohen’s κ coefficient was used to measure inter-rater agreement and calculated as κ = 0.7 (substantial agreement). Each video was watched 4 times, focusing on 1 of the 4 team members per viewing (senior surgeon, senior nurse, trainee surgeon, and trainee nurse). The frequency and types of leadership behavior of each of the 4 team members were recorded. Statistical significance to assess any differences was assessed using analysis of variance, whereby a p < 0.05 was taken to be significant. Leadership behaviors were triangulated with verbal cues and actions from the videos. Results All 3 scenarios were successfully completed. The mean scenario length was 22 minutes. A total of 362 leadership behaviors were recorded from the 12 participants. The most evident leadership behaviors of all team members were adhering to guidelines (which effectively equates to following Advanced Trauma and Life Support/Emergency Management of Severe Burns resuscitation guidelines and hence “maintaining standards”), followed by making decisions. Although in terms of total frequency the senior surgeon engaged in more leadership behaviors compared with the entire team, statistically there was no significant difference between all 4 members within the 8 leadership categories. This analysis highlights that “distributed leadership” was predominant, whereby leadership was “distributed” or “shared” among team members. The leadership behaviors within TBS also seemed to fall in line with the “direction, alignment, and commitment” ontology. Conclusions Effective leadership is essential for successful functioning of work teams and accomplishment of task goals. As the resuscitation of a patient with major burns is a dynamic event, team leaders require flexibility in their leadership behaviors to effectively adapt to changing situations. Understanding leadership behaviors of different team members within an authentic simulation can identify important behaviors required to optimize nontechnical skills in a major resuscitation. Furthermore, attempting to map these behaviors on to leadership models can help further our understanding of leadership theory. Collectively this can aid the development of refined simulation scenarios for team members, and can be extrapolated into other areas of simulation-based team training and interprofessional education.