Aligning business and IT from multi-level learning perspectives

Business and IT alignment is increasingly acknowledged as a key for organisational performance. However, alignment research lack to mechanisms that enable for on-going process with multi-level effects. Multi-level learning allows on-going effectiveness through development of the organisation and improved quality of business and IT strategies. In particular, exploration and exploitation enable effective process of alignment across dynamic multi-level of learning. Hence, this paper proposes a conceptual framework that links multi-level learning and business-IT strategy through the concept of exploration and exploitation, which considers short-term and long-term alignment together to address the challenges of strategic alignment faced in sustaining organisational performance.

A practical approach to a multi-level analysis with a sparse binary outcome within a large surgical trial

Objectives: To assess the potential source of variation that surgeon may add to patient outcome in a clinical trial of surgical procedures. Methods: Two large (n = 1380) parallel multicentre randomized surgical trials were undertaken to compare laparoscopically assisted hysterectomy with conventional methods of abdominal and vaginal hysterectomy; involving 43 surgeons. The primary end point of the trial was the occurrence of at least one major complication. Patients were nested within surgeons giving the data set a hierarchical structure. A total of 10% of patients had at least one major complication, that is, a sparse binary outcome variable. A linear mixed logistic regression model (with logit link function) was used to model the probability of a major complication, with surgeon fitted as a random effect. Models were fitted using the method of maximum likelihood in SAS((R)). Results: There were many convergence problems. These were resolved using a variety of approaches including; treating all effects as fixed for the initial model building; modelling the variance of a parameter on a logarithmic scale and centring of continuous covariates. The initial model building process indicated no significant 'type of operation' across surgeon interaction effect in either trial, the 'type of operation' term was highly significant in the abdominal trial, and the 'surgeon' term was not significant in either trial. Conclusions: The analysis did not find a surgeon effect but it is difficult to conclude that there was not a difference between surgeons. The statistical test may have lacked sufficient power, the variance estimates were small with large standard errors, indicating that the precision of the variance estimates may be questionable.

Multi-level Input Fusion in multi-modal systems

Context-aware multimodal interactive systems aim to adapt to the needs and behavioural patterns of users and offer a way forward for enhancing the efficacy and quality of experience (QoE) in human-computer interaction. The various modalities that constribute to such systems each provide a specific uni-modal response that is integratively presented as a multi-modal interface capable of interpretation of multi-modal user input and appropriately responding to it through dynamically adapted multi-modal interactive flow management , This paper presents an initial background study in the context of the first phase of a PhD research programme in the area of optimisation of data fusion techniques to serve multimodal interactivite systems, their applications and requirements.

A multi-level probabilistic neural network

Based on the idea of an important cluster, a new multi-level probabilistic neural network (MLPNN) is introduced. The MLPNN uses an incremental constructive approach, i.e. it grows level by level. The construction algorithm of the MLPNN is proposed such that the classification accuracy monotonically increases to ensure that the classification accuracy of the MLPNN is higher than or equal to that of the traditional PNN. Numerical examples are included to demonstrate the effectiveness of proposed new approach.

Disentangling multi-level systems: averaging, correlations and memory

We consider two weakly coupled systems and adopt a perturbative approach based on the Ruelle response theory to study their interaction. We propose a systematic way of parameterizing the effect of the coupling as a function of only the variables of a system of interest. Our focus is on describing the impacts of the coupling on the long term statistics rather than on the finite-time behavior. By direct calculation, we find that, at first order, the coupling can be surrogated by adding a deterministic perturbation to the autonomous dynamics of the system of interest. At second order, there are additionally two separate and very different contributions. One is a term taking into account the second-order contributions of the fluctuations in the coupling, which can be parameterized as a stochastic forcing with given spectral properties. The other one is a memory term, coupling the system of interest to its previous history, through the correlations of the second system. If these correlations are known, this effect can be implemented as a perturbation with memory on the single system. In order to treat this case, we present an extension to Ruelle's response theory able to deal with integral operators. We discuss our results in the context of other methods previously proposed for disentangling the dynamics of two coupled systems. We emphasize that our results do not rely on assuming a time scale separation, and, if such a separation exists, can be used equally well to study the statistics of the slow variables and that of the fast variables. By recursively applying the technique proposed here, we can treat the general case of multi-level systems.

A multi-level analytical approach for detection and visualization of intracellular NO production and nitrosation events using diaminofluoresceins

Diaminofluoresceins are widely used probes for detection and intracellular localization of NO formation in cultured/isolated cells and intact tissues. The fluorinated derivative, 4-amino-5-methylamino-2′,7′-difluorofluorescein (DAF-FM), has gained increasing popularity in recent years due to its improved NO-sensitivity, pH-stability, and resistance to photo-bleaching compared to the first-generation compound, DAF-2. Detection of NO production by either reagent relies on conversion of the parent compound into a fluorescent triazole, DAF-FM-T and DAF-2-T, respectively. While this reaction is specific for NO and/or reactive nitrosating species, it is also affected by the presence of oxidants/antioxidants. Moreover, the reaction with other molecules can lead to the formation of fluorescent products other than the expected triazole. Thus additional controls and structural confirmation of the reaction products are essential. Using human red blood cells as an exemplary cellular system we here describe robust protocols for the analysis of intracellular DAF-FM-T formation using an array of fluorescence-based methods (laser-scanning fluorescence microscopy, flow cytometry and fluorimetry) and analytical separation techniques (reversed-phase HPLC and LC-MS/MS). When used in combination, these assays afford unequivocal identification of the fluorescent signal as being derived from NO and are applicable to most other cellular systems without or with only minor modifications.

Multi-level planning for semi-autonomous vehicles in traffic scenarios based on separation maximization

The planning of semi-autonomous vehicles in traffic scenarios is a relatively new problem that contributes towards the goal of making road travel by vehicles free of human drivers. An algorithm needs to ensure optimal real time planning of multiple vehicles (moving in either direction along a road), in the presence of a complex obstacle network. Unlike other approaches, here we assume that speed lanes are not present and that different lanes do not need to be maintained for inbound and outbound traffic. Our basic hypothesis is to carry forward the planning task to ensure that a sufficient distance is maintained by each vehicle from all other vehicles, obstacles and road boundaries. We present here a 4-layer planning algorithm that consists of road selection (for selecting the individual roads of traversal to reach the goal), pathway selection (a strategy to avoid and/or overtake obstacles, road diversions and other blockages), pathway distribution (to select the position of a vehicle at every instance of time in a pathway), and trajectory generation (for generating a curve, smooth enough, to allow for the maximum possible speed). Cooperation between vehicles is handled separately at the different levels, the aim being to maximize the separation between vehicles. Simulated results exhibit behaviours of smooth, efficient and safe driving of vehicles in multiple scenarios; along with typical vehicle behaviours including following and overtaking.

A multi level analysis on pollination-related policies

The paper explores pollination from a multi level policy perspective and analyses the institutional fit and inter play of multi-faceted pollination-related policies. First, it asks what the major policies are that frame pollination at the EU level. Second, it explores the relationship between the EU policies and localised ways of understanding pollination. Addressed third is how the concept of ecosystem services can aid in under- standing the various ways of framing and governing the situation. The results show that the policy systems affecting pollination are abundant and that these systems create different kinds of pressure on stakeholders, at several levels of society. The local-level concerns are more about the loss of pollination services than about loss of pollinators. This points to the problem of fit between local activity driven by economic reasoning and biodiversity-driven EU policies. Here we see the concept of ecosystem services having some potential, since its operationalisation can combine economic and environmental considerations. Further- more, the analysis shows how, instead of formal institutions, it seems that social norms, habits, and motivation are the key to understanding and developing effective and attractive governance measures.

Occupational choice, socio-economic status and educational attainment: a study of the occupational choices and destinations of young people in the British household panel survey

The article considers young people's occupational choices at the age of 15 in relation to their educational attainment, the occupations of their parents and their actual occupations when they are in their early 20s. It uses data from the British Household Panel Survey over periods of between five and ten years. The young people in the survey are occupationally ambitious: many more aspire to professional, managerial and technical jobs than the likely availability of these occupations. In general ambitions and educational attainment and intentions are well aligned but there are also many instances of misalignment; either people wanting jobs which their educational attainments and intentions will not prepare them for, or people with less ambitious aspirations than their educational performance would justify. Children from more occupationally advantaged families are more ambitious, achieve better educationally and have better occupational outcomes than other children. However, where young people are both ambitious and educationally successful the occupational outcomes are as good for those from disadvantaged as advantaged families. In contrast, where young people are neither ambitious nor educationally successful, the outcomes for those from disadvantaged homes are very much poorer than for other young people. The article suggests that while choice is real it is also heavily constrained for many people. A possible educational implication of the study is that career interventions could be directed at under-ambitious but academically capable young people from disadvantaged backgrounds.

Dual technological development in Botswana agriculture: A Stochastic input distance function approach

To improve the welfare of the rural poor and keep them in the countryside, the government of Botswana has been spending 40% of the value of agricultural GDP on agricultural support services. But can investment make smallholder agriculture prosperous in such adverse conditions? This paper derives an answer by applying a two-output six-input stochastic translog distance function, with inefficiency effects and biased technical change to panel data for the 18 districts and the commercial agricultural sector, from 1979 to 1996 This model demonstrates that herds are the most important input, followed by draft power. land and seeds. Multilateral indices for technical change, technical efficiency and total factor productivity (TFP) show that the technology level of the commercial agricultural sector is more than six times that of traditional agriculture and that the gap has been increasing, due to technological regression in traditional agriculture and modest progress in commercial agriculture. Since the levels of efficiency are similar, the same patient is repeated by the TFP indices. This result highlights the policy dilemma of the trade-off between efficiency and equity objectives.

Population-level assessment of risks of pesticides to birds and mammals in the UK

It is generally acknowledged that population-level assessments provide,I better measure of response to toxicants than assessments of individual-level effects. population-level assessments generally require the use of models to integrate potentially complex data about the effects of toxicants on life-history traits, and to provide a relevant measure of ecological impact. Building on excellent earlier reviews we here briefly outline the modelling options in population-level risk assessment. Modelling is used to calculate population endpoints from available data, which is often about Individual life histories, the ways that individuals interact with each other, the environment and other species, and the ways individuals are affected by pesticides. As population endpoints, we recommend the use of population abundance, population growth rate, and the chance of population persistence. We recommend two types of model: simple life-history models distinguishing two life-history stages, juveniles and adults; and spatially-explicit individual-based landscape models. Life-history models are very quick to set up and run, and they provide a great deal or insight. At the other extreme, individual-based landscape models provide the greatest verisimilitude, albeit at the cost of greatly increased complexity. We conclude with a discussion of the cations of the severe problems of parameterising models.

A framework for agricultural adaptation to climate change in Southern Nigeria

The agricultural sector which contributes between 20-50% of gross domestic product in Africa and employs about 60% of the population is greatly affected by climate change impacts. Agricultural productivity and food prices are expected to rise due to this impact thereby worsening the food insecurity and poor nutritional health conditions in the continent. Incidentally, the capacity in the continent to adapt is very low. Addressing these challenges will therefore require a holistic and integrated adaptation framework hence this study. A total of 360 respondents selected through a multi-stage random sampling technique participated in the study that took place in Southern Nigeria from 2008-2011. Results showed that majority of respondents (84%) were aware that some climate change characteristics such as uncertainties at the onset of farming season, extreme weather events including flooding and droughts, pests, diseases, weed infestation, and land degradation have all been on the increase. The most significant effects of climate change that manifested in the area were declining soil fertility and weed infestation. Some of the adaptation strategies adopted by farmers include increased weeding, changing the timing of farm operations, and processing of crops to reduce post-harvest losses. Although majority of respondents were aware of government policies aimed at protecting the environment, most of them agreed that these policies were not being effectively implemented. A mutually inclusive framework comprising of both indigenous and modern techniques, processes, practices and technologies was then developed from the study in order to guide farmers in adapting to climate change effects/impacts.

A conceptual framework for aligning business and information systems from organisational learning perspective

The three decades of on-going executives’ concerns of how to achieve successful alignment between business and information technology shows the complexity of such a vital process. Most of the challenges of alignment are related to knowledge and organisational change and several researchers have introduced a number of mechanisms to address some of these challenges. However, these mechanisms pay less attention to multi-level effects, which results in a limited un-derstanding of alignment across levels. Therefore, we reviewed these challenges from a multi-level learning perspective and found that business and IT alignment is related to the balance of exploitation and exploration strategies with the intellec-tual content of individual, group and organisational levels.

Biologically motivated model for object detection and identification in real-world scenes

The classical computer vision methods can only weakly emulate some of the multi-level parallelisms in signal processing and information sharing that takes place in different parts of the primates’ visual system thus enabling it to accomplish many diverse functions of visual perception. One of the main functions of the primates’ vision is to detect and recognise objects in natural scenes despite all the linear and non-linear variations of the objects and their environment. The superior performance of the primates’ visual system compared to what machine vision systems have been able to achieve to date, motivates scientists and researchers to further explore this area in pursuit of more efficient vision systems inspired by natural models. In this paper building blocks for a hierarchical efficient object recognition model are proposed. Incorporating the attention-based processing would lead to a system that will process the visual data in a non-linear way focusing only on the regions of interest and hence reducing the time to achieve real-time performance. Further, it is suggested to modify the visual cortex model for recognizing objects by adding non-linearities in the ventral path consistent with earlier discoveries as reported by researchers in the neuro-physiology of vision.

Extraction of tidal channel networks from aerial photographs alone and combined with laser altimetry

Tidal channel networks play an important role in the intertidal zone, exerting substantial control over the hydrodynamics and sediment transport of the region and hence over the evolution of the salt marshes and tidal flats. The study of the morphodynamics of tidal channels is currently an active area of research, and a number of theories have been proposed which require for their validation measurement of channels over extensive areas. Remotely sensed data provide a suitable means for such channel mapping. The paper describes a technique that may be adapted to extract tidal channels from either aerial photographs or LiDAR data separately, or from both types of data used together in a fusion approach. Application of the technique to channel extraction from LiDAR data has been described previously. However, aerial photographs of intertidal zones are much more commonly available than LiDAR data, and most LiDAR flights now involve acquisition of multispectral images to complement the LiDAR data. In view of this, the paper investigates the use of multispectral data for semiautomatic identification of tidal channels, firstly from only aerial photographs or linescanner data, and secondly from fused linescanner and LiDAR data sets. A multi-level, knowledge-based approach is employed. The algorithm based on aerial photography can achieve a useful channel extraction, though may fail to detect some of the smaller channels, partly because the spectral response of parts of the non-channel areas may be similar to that of the channels. The algorithm for channel extraction from fused LiDAR and spectral data gives an increased accuracy, though only slightly higher than that obtained using LiDAR data alone. The results illustrate the difficulty of developing a fully automated method, and justify the semi-automatic approach adopted.