Learning across business sectors: Knowledge sharing between aerospace and construction

This report addresses the extent that managerial practices can be shared between the aerospace and construction sectors. Current recipes for learning from other industries tend to be oversimplistic and often fail to recognise the embedded and contextual nature of managerial knowledge. Knowledge sharing between business sectors is best understood as an essential source of innovation. The process of comparison challenges assumptions and better equips managers to cope with future change. Comparisons between the aerospace and construction sectors are especially useful because they are so different. The two sectors differ hugely in terms of their institutional context, structure and technological intensity. The aerospace sector has experienced extensive consolidation and is dominated by a small number of global companies. Aerospace companies operate within complex networks of global interdependency such that collaborative working is a commercial imperative. In contrast, the construction sector remains highly fragmented and is characterised by a continued reliance on small firms. The vast majority of construction firms compete within localised markets that are too often characterised by opportunistic behaviour. Comparing construction to aerospace highlights the unique characteristics of both sectors and helps explain how managerial practices are mediated by context. Detailed comparisons between the two sectors are made in a range of areas and guidance is provided for the implementation of knowledge sharing strategies within and across organisations. The commonly accepted notion of ‘best practice’ is exposed as a myth. Indeed, universal models of best practice can be detrimental to performance by deflecting from the need to adapt continuously to changing circumstances. Competitiveness in the construction sector too often rests on efficiency in managing contracts, with a particular emphasis on the allocation of risk. Innovation in construction tends to be problem-driven and is rarely shared from project to project. In aerospace, the dominant model of competitiveness means that firms have little choice other than to invest in continuous innovation, despite difficult trading conditions. Research and development (R&D) expenditure in aerospace continues to rise as a percentage of turnovers. A sustained capacity for innovation within the aerospace sector depends crucially upon stability and continuity of work. In the construction sector, the emergence of the ‘hollowed-out’ firm has undermined the industry’s capacity for innovation. Integrated procurement contexts such as prime contracting in construction potentially provide a more supportive climate for an innovation-based model of competitiveness. However, investment in new ways of working depends upon a shift in thinking not only amongst construction contractors, but also amongst the industry’s major clients.

Monetary equilibrium with decentralized trade and learning

This paper analyzes the stability of monetary regimes in an economy where fiat money is endogenously created by the government, information about its value is imperfect, and learning is decentralized. We show that monetary stability depends crucially on the speed of information transmission in the economy. Our model generates a dynamic on the acceptability of fiat money that resembles historical accounts of the rise and eventual collapse of overissued paper money. It also provides an explanation of the fact that, despite its obvious advantages, the widespread use of fiat money is only a recent development.

A note on learning chaotic sunspot equilibrium

In this paper we prove convergence to chaotic sunspot equilibrium through two learning rules used in the bounded rationality literature. The rst one shows the convergence of the actual dynamics generated by simple adaptive learning rules to a probability distribution that is close to the stationary measure of the sunspot equilibrium; since this stationary measure is absolutely continuous it results in a robust convergence to the stochastic equilibrium. The second one is based on the E-stability criterion for testing stability of rational expectations equilibrium, we show that the conditional probability distribution de ned by the sunspot equilibrium is expectational stable under a reasonable updating rule of this parameter. We also report some numerical simulations of the processes proposed.

Mensuração de estratégias de estudo e aprendizagem de alunos universitários: learning and study strategies inventory (lassi) adaptação e validação para o Brasil

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Does the Presence of a Learning Disability Elicit a Stigmatization?

Aims: To determine whether or not a Learning Disability(LD) label leads to stigmatization. Study Design: This research used a 2(sex of participant) x 2(LD label)x 2 (Sex of stimulus person) factorial design. Place and Duration of Study: Bucknell University, between October 2010 and April 2011. Methodology: Sample: We included 200 participants (137 women and 63 men, ranging in age from 18 – 75 years, M = 26.41. Participants rated the stimulus individual on 27 personality traits, 8 Life success measures, and the Big-5 personality dimensions. Also, participants completed a Social Desirability measure. Results: A MANOVA revealed a main effect for the Learning Disability description, F(6, 185) = 6.41 p< .0001, eta2 = .17,for the Big-5 personality dimensions, Emotional Stability, F(1, 185) = 13.39, p < .001, eta2 = .066, and Openness to Experiences F(1,185) = 7.12, p< .008, eta2 = .036.Stimulus individuals described as having a learning disability were perceived as being less emotionally stable and more open to experiences than those described as not having a learning disability. Another MANOVA revealed a main effect for having a disability or not, F(8, 183) = 4.29, p< .0001, eta2 = .158, for the Life Success items, Attractiveness, F(1, 198) = 16.63, p< .0001, eta2 = .080, and Future Success,F(1, 198) = 4.57, p< .034, eta2 = .023. Stimulus individuals described as having a learning disability were perceived as being less attractive and with less potential for success than those described as not having a learning disability. Conclusion: The results of this research provide evidence that a bias exists toward those who have learning disabilities. The mere presence of an LD label had the ability to cause a differential perception of those with LDs and those without LDs.

Agrarian learning processes in Switzerland 1918–1939

This paper examines the social impacts of weather extremes and the processes of social and communicative learning a society undertakes to find alternative ways to deal with the consequences of a crisis. In the beginning of the 20th Century hunger seemed to be expelled from Europe. Switzerland – like many other European countries – was involved in a global interdependent trade system, which provided necessary goods. But at the end of World War I very cold and wet summers in 1916/17 (causing crop failure) and the difficulties in war-trade led to malnutrition and enormous price risings of general living-standards in Switzerland, which shocked the people and caused revolutionary uprisings in 1918. The experience of malnutrition during the last two years of war made clear that the traditional ways of food supply in Switzerland lacked crisis stability. Therefore various agents in the field of food production, distribution and consumption searched for alternative ways of food supply. In that sense politicians, industrialists, consumer-groups, left-wing communitarians and farmers developed several strategies for new ways in food production. Traditionally there were political conflicts in Switzerland between farmers and consumers regarding price policies, which led mainly to the conflict in 1918. Consumers accused famers of holding back food to control extortionate prices while the farmers pointed to the bad harvest causing the price rising. The collaboration of these groups in search for new forms of food-stability made social integration possible again. In addition to other crisis-factors, weather extremes can have disastrous impacts and destroy a society’s self-confidence to its core. But even such crisis can lead to processes of substantial learning that allows a regeneration of confidence and show positive influence on political stabilization. The paper focuses on the process of learning and the alternative methods of food production that were suggested by various agents working in the field during the Interwar period. To achieve that goal documents of the various associations are analyzed and newspapers have been taken into consideration. Through the method of discourse-analysis of food-production during the Interwar period, possible solutions that crossed the minds of the agents should be brought to light.

Emotional stability promotes intelligence

Emotional stability promotes intelligence In the research field of the relationship between intelligence and personality factors, one of the most consistent findings is that intelligence is positively correlated with emotional stability. However, few studies have considered this relationship in children, and very few have differentiated between types of intelligence as well as underlying differences in working memory capacity when explaining the relationship between intelligence scores and emotional stability. In this study, the level of emotional stability and performance in a proxy for fluid and crystallized intelligence as well as in two working memory tasks was assessed in a sample of 397 primary school children. Results reveal that emotional stability is significantly positively related to vocabulary (crystallized intelligence), moderated by high working memory performance, but unrelated to abstract reasoning (fluid intelligence). This was interpreted as indicating that the positive relationship between intelligence and emotional stability is mainly due to learning advantages starting in early age, due to high working memory performance, rather than to higher general intelligence. This bears the important implication that emotionally labile children (high level of neuroticism) should be supported to regulate their negative emotions, intrusive thoughts and anxiety as early as possible to eliminate progressive learning disadvantages. One approach to do so is by specific working memory training targeting the improvement of emotional regulation skills.

A feasibility study of self-learning adaptive flight control for high performance aircraft

"Technical report AFFDL-TR-67-18"

Understanding institutional conversion:the case of the National Reporting and Learning System

This article focuses on one type of institutional change: conversion. One innovative approach to institutional change, the “political-coalitional approach”, acknowledges that: institutions can have unintended effects, which may privilege certain groups over others; institutions are often created and sustained through compromise with external actors; and institutions’ external context can vary significantly over time, as different coalitions’ power waxes and wanes. This approach helps explain the conversion of one institution drawn from the UK National Health Service, the National Reporting and Learning System. However, the shift of this system from producing formative information to facilitate learning to promote safer care, towards producing summative information to support resource allocation decisions, cannot be explained merely by examining the actions of external power coalitions. An internal focus, which considers factors that are normally viewed as “organisational” (such as leadership and internal stability), is also required.

Learning from mother nature:exploiting a biological antioxidant for the melt stabilisation of polymers

An overview of the antioxidant role of the biologically active form of vitamin E, α-tocopherol, in polyolefins is discussed. The effect of the vitamin antioxidant on the melt and colour stability of polyethylene (PE) and polypropylene (PP) is highlighted. It is shown that tocopherol is a highly effective antioxidant that results in superior melt stabilisation of polyolefins particularly when used at much lower concentration than that needed for conventional synthetic hindered phenol processing stabilisers. As with other hindered phenols,α-tocopherol imparts also some colour to the polymer but this is shown to be reduced drastically in the presence of other antioxidants, such as phosphites, or other additives, such as polyhydric alcohols.

Noradrenergic modulatoins of odor learning and odor representation in the rat

How experience alters neuronal ensemble dynamics and how locus coeruleus-mediated norepinephrine release facilitates memory formation in the brain are the topics of this thesis. Here we employed a visualization technique, cellular compartment analysis of temporal activity by fluorescence in situ hybridization (catFISH), to assess activation patterns of neuronal ensembles in the olfactory bulb (OB) and anterior piriform cortex (aPC) to repeated odor inputs. Two associative learning models were used, early odor preference learning in rat pups and adult rat go-no-go odor discrimination learning. With catFISH of an immediate early gene, Arc, we showed that odor representation in the OB and aPC was sparse (~5-10%) and widely distributed. Odor associative learning enhanced the stability of the rewarded odor representation in the OB and aPC. The stable component, indexed by the overlap between the two ensembles activated by the rewarded odor at two time points, increased from ~25% to ~50% (p = 0.004-1.43E⁻4; Chapter 3 and 4). Adult odor discrimination learning promoted pattern separation between rewarded and unrewarded odor representations in the aPC. The overlap between rewarded and unrewarded odor representations reduced from ~25% to ~14% (p = 2.28E⁻⁵). However, learning an odor mixture as a rewarded odor increased the overlap of the component odor representations in the aPC from ~23% to ~44% (p = 0.010; Chapter 4). Blocking both α- and β-adrenoreceptors in the aPC prevented highly similar odor discrimination learning in adult rats, and reduced OB mitral and granule ensemble stability to the rewarded odor. Similar treatment in the OB only slowed odor discrimination learning. However, OB adrenoceptor blockade disrupted pattern separation and ensemble stability in the aPC when the rats demonstrated deficiency in discrimination (Chapter 5). In another project, the role of α₂-adrenoreceptors in the OB during early odor preference learning was studied. OB α2-adrenoceptor activation was necessary for odor learning in rat pups. α₂-adrenoceptor activation was additive with β-adrenoceptor mediated signalling to promote learning (Chapter 2). Together, these experiments suggest that odor representations are highly adaptive at the early stages of odor processing. The OB and aPC work in concert to support odor learning and top-down adrenergic input exerts a powerful modulation on both learning and odor representation.

Inlärningseffekten på Limits of Stability på Transtibialt Amputerade Protesbrukare

Syftet med studien är att undersöka om det finns någon inlärningseffekt på testet Limits of Stability (LoS) för transtibialt amputerade protesbrukare och en kontrollgrupp. Sju transtibialt amputerade protesbrukare och en kontrollgrupp bestående av sju friska vuxna män upprepade testet LoS  fem gånger under fyra testtillfällen. Två kraftplattor och 69 reflexmarkörer användes för att samla in data. Testpersonerna placerades med en fot på varje kraftplatta och blev instruerade att förflytta sin center of pressure genom att luta kroppen från anklarna mot åtta mål som visades på en skärm tillsammans med deras center of pressure. Ordningen på målen var slumpvist utvalda. Datan analyserades med Friedmans test, eftersom den inte var normalfördelad, för att se om det fanns någon skillnad i resultatet mellan upprepningarna av testet och resultatet mellan testtillfällena. Det fanns några signifikanta skillnader mellan upprepningarna och mellan testtillfällena som tyder på att det finns en inlärningseffekt efter första upprepningen och första testtillfället, men resultatet var inte tillräckligt entydigt för att kunna dra några konkreta slutsatser. Vidare studier rekommenderas.

Automatic learning of pre-miRNAs from different species.

Discovery of microRNAs (miRNAs) relies on predictive models for characteristic features from miRNA precursors (pre-miRNAs). The short length of miRNA genes and the lack of pronounced sequence features complicate this task. To accommodate the peculiarities of plant and animal miRNAs systems, tools for both systems have evolved differently. However, these tools are biased towards the species for which they were primarily developed and, consequently, their predictive performance on data sets from other species of the same kingdom might be lower. While these biases are intrinsic to the species, their characterization can lead to computational approaches capable of diminishing their negative effect on the accuracy of pre-miRNAs predictive models. We investigate in this study how 45 predictive models induced for data sets from 45 species, distributed in eight subphyla/classes, perform when applied to a species different from the species used in its induction. Results: Our computational experiments show that the separability of pre-miRNAs and pseudo pre-miRNAs instances is species-dependent and no feature set performs well for all species, even within the same subphylum/class. Mitigating this species dependency, we show that an ensemble of classifiers reduced the classification errors for all 45 species. As the ensemble members were obtained using meaningful, and yet computationally viable feature sets, the ensembles also have a lower computational cost than individual classifiers that rely on energy stability parameters, which are of prohibitive computational cost in large scale applications. Conclusion: In this study, the combination of multiple pre-miRNAs feature sets and multiple learning biases enhanced the predictive accuracy of pre-miRNAs classifiers of 45 species. This is certainly a promising approach to be incorporated in miRNA discovery tools towards more accurate and less species-dependent tools.

A machine learning based method to detect genomic imbalances exploiting X chromosome exome reads

Whole Exome Sequencing (WES) is rapidly becoming the first-tier test in clinics, both thanks to its declining costs and the development of new platforms that help clinicians in the analysis and interpretation of SNV and InDels. However, we still know very little on how CNV detection could increase WES diagnostic yield. A plethora of exome CNV callers have been published over the years, all showing good performances towards specific CNV classes and sizes, suggesting that the combination of multiple tools is needed to obtain an overall good detection performance. Here we present TrainX, a ML-based method for calling heterozygous CNVs in WES data using EXCAVATOR2 Normalized Read Counts. We select males and females’ non pseudo-autosomal chromosome X alignments to construct our dataset and train our model, make predictions on autosomes target regions and use HMM to call CNVs. We compared TrainX against a set of CNV tools differing for the detection method (GATK4 gCNV, ExomeDepth, DECoN, CNVkit and EXCAVATOR2) and found that our algorithm outperformed them in terms of stability, as we identified both deletions and duplications with good scores (0.87 and 0.82 F1-scores respectively) and for sizes reaching the minimum resolution of 2 target regions. We also evaluated the method robustness using a set of WES and SNP array data (n=251), part of the Italian cohort of Epi25 collaborative, and were able to retrieve all clinical CNVs previously identified by the SNP array. TrainX showed good accuracy in detecting heterozygous CNVs of different sizes, making it a promising tool to use in a diagnostic setting.

The three-dimensional single-bin-size bin packing problem: combining metaheuristic and machine learning approaches

The Three-Dimensional Single-Bin-Size Bin Packing Problem is one of the most studied problem in the Cutting & Packing category. From a strictly mathematical point of view, it consists of packing a finite set of strongly heterogeneous “small” boxes, called items, into a finite set of identical “large” rectangles, called bins, minimizing the unused volume and requiring that the items are packed without overlapping. The great interest is mainly due to the number of real-world applications in which it arises, such as pallet and container loading, cutting objects out of a piece of material and packaging design. Depending on these real-world applications, more objective functions and more practical constraints could be needed. After a brief discussion about the real-world applications of the problem and a exhaustive literature review, the design of a two-stage algorithm to solve the aforementioned problem is presented. The algorithm must be able to provide the spatial coordinates of the placed boxes vertices and also the optimal boxes input sequence, while guaranteeing geometric, stability, fragility constraints and a reduced computational time. Due to NP-hard complexity of this type of combinatorial problems, a fusion of metaheuristic and machine learning techniques is adopted. In particular, a hybrid genetic algorithm coupled with a feedforward neural network is used. In the first stage, a rich dataset is created starting from a set of real input instances provided by an industrial company and the feedforward neural network is trained on it. After its training, given a new input instance, the hybrid genetic algorithm is able to run using the neural network output as input parameter vector, providing as output the optimal solution. The effectiveness of the proposed works is confirmed via several experimental tests.