A multi-level investigation of psychological contract breach and organizational identification through the lens of perceived organizational membership:testing a moderated-mediated model

Drawing on the perceived organizational membership theoretical framework and the social identity view of dissonance theory, I examined in this study the dynamics of the relationship between psychological contract breach and organizational identification. I included group-level transformational and transactional leadership as well as procedural justice in the hypothesized model as key antecedents for organizational membership processes. I further explored the mediating role of psychological contract breach in the relationship between leadership, procedural justice climate, and organizational identification and proposed separateness–connectedness self-schema as an important moderator of the above mediated relationship. Hierarchical linear modeling results from a sample of 864 employees from 162 work units in 10 Greek organizations indicated that employees' perception of psychological contract breach negatively affected their organizational identification. I also found psychological contract breach to mediate the impact of transformational and transactional leadership on organizational identification. Results further provided support for moderated mediation and showed that the indirect effects of transformational and transactional leadership on identification through psychological contract breach were stronger for employees with a low connectedness self-schema.

Incremental tensor biased discriminant analysis:a new color-based visual tracking method

Most existing color-based tracking algorithms utilize the statistical color information of the object as the tracking clues, without maintaining the spatial structure within a single chromatic image. Recently, the researches on the multilinear algebra provide the possibility to hold the spatial structural relationship in a representation of the image ensembles. In this paper, a third-order color tensor is constructed to represent the object to be tracked. Considering the influence of the environment changing on the tracking, the biased discriminant analysis (BDA) is extended to the tensor biased discriminant analysis (TBDA) for distinguishing the object from the background. At the same time, an incremental scheme for the TBDA is developed for the tensor biased discriminant subspace online learning, which can be used to adapt to the appearance variant of both the object and background. The experimental results show that the proposed method can track objects precisely undergoing large pose, scale and lighting changes, as well as partial occlusion. © 2009 Elsevier B.V.

The antecedents and consequences of anticipatory identification

The aim of this thesis was to investigate anticipatory identification: newcomers’ identification with an organisation prior to entry; in particular by exploring the antecedents and consequences of the construct. Although organisational identification has been frequently investigated over the past 25 years, surprisingly little is known about what causes an individual to identify with a new organisation before entry and whether this has an impact on their relationship with the organisation after formally taking up membership. Drawing on a Social Identity approach to organisational identification, it was hypothesised that newcomers would more closely identify with an organisation prior to entry when the organisation was seen as a source of positive social identity and was situationally relevant and meaningful to the newcomer, i.e. salient, during the pre-entry period. It was also hypothesised that anticipatory identification would have post-entry consequences and would predict newcomers’ post-entry identification, turnover intentions and job satisfaction. An indirect relationship between anticipatory identification and post-entry identification through post-entry social identity judgements (termed a “feedback loop” mechanism) was additionally proposed. Finally anticipatory identification was also predicted to moderate the relationship between post-entry social identity judgements and post-entry identification (termed a “buffering” mechanism). Four studies were conducted to test these hypotheses. Study One served as a pilot study, using a retrospective self-report design with s sample of 124 university students to initially test the proposed conceptual model. Studies Two and Three adopted experimental designs. Each used a unique sample of 72 staff and students from Aston University to respectively test the hypothesised positive social identity motive and salience antecedents of anticipatory identification. Study Four explored the relationship between anticipatory identification, its antecedents and consequences longitudinally, using an organisational sample of 45 employees. Overall, these studies found support for a social identity motive antecedent of anticipatory identification, as well as more limited evidence that anticipatory identification was associated with the salience of an organisation prior to entry. Support was inconsistent for a direct relationship between anticipatory identification and post-entry identification and there was no evidence that anticipatory identification was a significant direct predictor of turnover intention and job satisfaction. Anticipatory identification was however found to act as a buffer in the relationship between post-entry social identity judgements and post-entry identification in all but one of the four samples measured. A feedback loop mechanism was observed within the experimental designs of Studies Two and Three, but not within the organisational samples of Studies One and Four. Overall the findings of these four studies highlight key ways through which anticipatory identification can develop prior to entry into an organisation. Moreover, the research observed several important post-entry consequences of anticipatory identification, indicating that an understanding of post-entry identification may be enriched by attending more closely to the extent to which newcomers identify with an organisation prior to entry.

How different are the visual representations used for object recognition in middle childhood and adulthood?

Recent experimental studies have shown that development towards adult performance levels in configural processing in object recognition is delayed through middle childhood. Whilst partchanges to animal and artefact stimuli are processed with similar to adult levels of accuracy from 7 years of age, relative size changes to stimuli result in a significant decrease in relative performance for participants aged between 7 and 10. Two sets of computational experiments were run using the JIM3 artificial neural network with adult and 'immature' versions to simulate these results. One set progressively decreased the number of neurons involved in the representation of view-independent metric relations within multi-geon objects. A second set of computational experiments involved decreasing the number of neurons that represent view-dependent (nonrelational) object attributes in JIM3's Surface Map. The simulation results which show the best qualitative match to empirical data occurred when artificial neurons representing metric-precision relations were entirely eliminated. These results therefore provide further evidence for the late development of relational processing in object recognition and suggest that children in middle childhood may recognise objects without forming structural description representations.

Biosynthesis and analysis of plant oxylipins

The term oxylipin is applied to the generation of oxygenated products of polyunsaturated fatty acids that can arise either through non-enzymatic or enzymatic processes generating a complex array of products, including alcohols, aldehydes, ketones, acids and hydrocarbon gases. The biosynthetic origin of these products has revealed an array of enzymes involved in their formation and more recently a radical pathway. These include lipoxygenases and α-dioxygenase that insert both oxygen atoms in to the acyl chain to initiate the pathways, to specialised P450 monooxygenases that are responsible for their downstream processing. This latter group include enzymes at the branch points such as allene oxide synthase, leading to jasmonate signalling, hydroperoxide lyase, responsible for generating pathogen/pest defensive volatiles and divinyl ether synthases and peroxygenases involved in the formation of antimicrobial compounds. The complexity of the products generated raises significant challenges for their rapid identification and quantification using metabolic screening methods. Here the current developments in oxylipin metabolism are reviewed together with the emerging technologies required to expand this important field of research that underpins advances in plant-pest/pathogen interactions.

Racioethnic differences in job satisfaction: A test of orthogonal cultural identification theory and self -categorization theory

The theories of orthogonal cultural identification and self-categorization are offered as links in examining the possible racioethnic differences in job satisfaction. It is posited that racioethnicity (Cox & Blake, 1991) is multidimensional with at least three conceptually distinct dimensions. Since there is a need for consistent terminology with respect to these distinct dimensions, the following new terms are offered to differentiate among them: "physioethnicity" refers to the physiological dimension of racioethnicity; "socioethnicity" refers to the sociocultural dimension; and "psychoethnicity" refers to the psychological dimension.^ Results showed that for the dominant group (Hispanics in this case) (1) bicultural and multicultural individuals were more satisfied with coworkers than acultural and monocultural individuals and (2) individuals with higher strength of psychoethnicity were more satisfied with coworkers, the work itself, and supervision than those with lower strength of psychoethnicity. The findings suggest racioethnic differences within the dominant group and between groups beyond race. ^

Fleshing Out and Losing Flesh: The Decomposing Corpse as Narrative Object in American Hardboiled Detective Fiction

This dissertation examines the corpse as an object in and of American hardboiled detective fiction written between 1920 and 1950. I deploy several theoretical frames, including narratology, body-as-text theory, object relations theory, and genre theory, in order to demonstrate the significance of objects, symbols, and things primarily in the clever and crafty work of Dashiell Hammett (1894-1961) and Raymond Chandler (1888-1959), but also touching on the writings of their lesser known accomplices. I construct a literary genealogy of American hardboiled detective fiction originating in the writings of Edgar Allan Poe, compare the contributions of classic or Golden Age detective fiction in England, and describe the socio-economic contexts, particularly the predominance of the “pulps,” that gave birth to the realism of the Hardboiled School. Taking seriously Chandler’s obsession with the art of murder, I engage with how authors pre-empt their readers’ knowledge of the tricks of the trade and manipulate their expectations, as well as discuss the characteristics and effect of the inimitable hardboiled style, its sharpshooting language and deadpan humour. Critical scholarship has rarely addressed the body and figure of the corpse, preferring to focus instead on the machinations of the femme fatale, the performance of masculinity, or the prevalence of violence. I cast new light on the world of hardboiled detective fiction by dissecting the corpse as the object that both motivates and de-composes (or rots away from) the narrative that makes it signify. I treat the corpse as an inanimate object, indifferent to representation, that destabilizes the integrity and self-possession, as well as the ratiocination, of the detective who authors the narrative of how the corpse came to be. The corpse is all deceptive and dangerous surface rather than the container of hidden depths of life and meaning that the detective hopes to uncover and reconstruct. I conclude with a chapter that is both critical denouement and creative writing experiment to reveal the self-reflexive (and at times metafictional) dimensions of hardboiled fiction. My dissertation, too, in the manner of hardboiled fiction, hopes to incriminate my readers as much as enlighten them.

Automatic Identification of Large Fragments in a Pile of Broken Rock Using a Time-of-Flight Camera

This paper presents a solution to part of the problem of making robotic or semi-robotic digging equipment less dependant on human supervision. A method is described for identifying rocks of a certain size that may affect digging efficiency or require special handling. The process involves three main steps. First, by using range and intensity data from a time-of-flight (TOF) camera, a feature descriptor is used to rank points and separate regions surrounding high scoring points. This allows a wide range of rocks to be recognized because features can represent a whole or just part of a rock. Second, these points are filtered to extract only points thought to belong to the large object. Finally, a check is carried out to verify that the resultant point cloud actually represents a rock. Results are presented from field testing on piles of fragmented rock. Note to Practitioners—This paper presents an algorithm to identify large boulders in a pile of broken rock as a step towards an autonomous mining dig planner. In mining, piles of broken rock can contain large fragments that may need to be specially handled. To assess rock piles for excavation, we make use of a TOF camera that does not rely on external lighting to generate a point cloud of the rock pile. We then segment large boulders from its surface by using a novel feature descriptor and distinguish between real and false boulder candidates. Preliminary field experiments show promising results with the algorithm performing nearly as well as human test subjects.

Upper-Pleistocene terrace deposits in Mediterranean climate: geomorphological and source-rock control on mineral and geochemical signatures (Betic Cordillera, SE Spain)

Mineral and chemical composition of alluvial Upper-Pleistocene deposits from the Alto Guadalquivir Basin (SE Spain) were studied as a tool to identify sedimentary and geomorphological processes controlling its formation. Sediments located upstream, in the north-eastern sector of the basin, are rich in dolomite, illite, MgO and KB2BO. Downstream, sediments at the sequence base are enriched in calcite, smectite and CaO, whereas the upper sediments have similar features to those from upstream. Elevated rare-earth elements (REE) values can be related to low carbonate content in the sediments and the increase of silicate material produced and concentrated during soil formation processes in the neighbouring source areas. Two mineralogical and geochemical signatures related to different sediment source areas were identified. Basal levels were deposited during a predominantly erosive initial stage, and are mainly composed of calcite and smectite materials enriched in REE coming from Neogene marls and limestones. Then the deposition of the upper levels of the alluvial sequences, made of dolomite and illitic materials depleted in REE coming from the surrounding Sierra de Cazorla area took place during a less erosive later stage of the fluvial system. Such modification was responsible of the change in the mineralogical and geochemical composition of the alluvial sediments.

Signal subspace identification in hyperspectral imagery

Terrestrial remote sensing imagery involves the acquisition of information from the Earth's surface without physical contact with the area under study. Among the remote sensing modalities, hyperspectral imaging has recently emerged as a powerful passive technology. This technology has been widely used in the fields of urban and regional planning, water resource management, environmental monitoring, food safety, counterfeit drugs detection, oil spill and other types of chemical contamination detection, biological hazards prevention, and target detection for military and security purposes [2-9]. Hyperspectral sensors sample the reflected solar radiation from the Earth surface in the portion of the spectrum extending from the visible region through the near-infrared and mid-infrared (wavelengths between 0.3 and 2.5 µm) in hundreds of narrow (of the order of 10 nm) contiguous bands [10]. This high spectral resolution can be used for object detection and for discriminating between different objects based on their spectral xharacteristics [6]. However, this huge spectral resolution yields large amounts of data to be processed. For example, the Airbone Visible/Infrared Imaging Spectrometer (AVIRIS) [11] collects a 512 (along track) X 614 (across track) X 224 (bands) X 12 (bits) data cube in 5 s, corresponding to about 140 MBs. Similar data collection ratios are achieved by other spectrometers [12]. Such huge data volumes put stringent requirements on communications, storage, and processing. The problem of signal sbspace identification of hyperspectral data represents a crucial first step in many hypersctral processing algorithms such as target detection, change detection, classification, and unmixing. The identification of this subspace enables a correct dimensionality reduction (DR) yelding gains in data storage and retrieval and in computational time and complexity. Additionally, DR may also improve algorithms performance since it reduce data dimensionality without losses in the useful signal components. The computation of statistical estimates is a relevant example of the advantages of DR, since the number of samples required to obtain accurate estimates increases drastically with the dimmensionality of the data (Hughes phnomenon) [13].

Leveraging Multiple Features for Image Retrieval and Matching

The goal of image retrieval and matching is to find and locate object instances in images from a large-scale image database. While visual features are abundant, how to combine them to improve performance by individual features remains a challenging task. In this work, we focus on leveraging multiple features for accurate and efficient image retrieval and matching. We first propose two graph-based approaches to rerank initially retrieved images for generic image retrieval. In the graph, vertices are images while edges are similarities between image pairs. Our first approach employs a mixture Markov model based on a random walk model on multiple graphs to fuse graphs. We introduce a probabilistic model to compute the importance of each feature for graph fusion under a naive Bayesian formulation, which requires statistics of similarities from a manually labeled dataset containing irrelevant images. To reduce human labeling, we further propose a fully unsupervised reranking algorithm based on a submodular objective function that can be efficiently optimized by greedy algorithm. By maximizing an information gain term over the graph, our submodular function favors a subset of database images that are similar to query images and resemble each other. The function also exploits the rank relationships of images from multiple ranked lists obtained by different features. We then study a more well-defined application, person re-identification, where the database contains labeled images of human bodies captured by multiple cameras. Re-identifications from multiple cameras are regarded as related tasks to exploit shared information. We apply a novel multi-task learning algorithm using both low level features and attributes. A low rank attribute embedding is joint learned within the multi-task learning formulation to embed original binary attributes to a continuous attribute space, where incorrect and incomplete attributes are rectified and recovered. To locate objects in images, we design an object detector based on object proposals and deep convolutional neural networks (CNN) in view of the emergence of deep networks. We improve a Fast RCNN framework and investigate two new strategies to detect objects accurately and efficiently: scale-dependent pooling (SDP) and cascaded rejection classifiers (CRC). The SDP improves detection accuracy by exploiting appropriate convolutional features depending on the scale of input object proposals. The CRC effectively utilizes convolutional features and greatly eliminates negative proposals in a cascaded manner, while maintaining a high recall for true objects. The two strategies together improve the detection accuracy and reduce the computational cost.

Activity-based life cycle costing model for cost estimation and monitoring – Case: manned and autonomous dry bulk carrier

Recent developments in automation, robotics and artificial intelligence have given a push to a wider usage of these technologies in recent years, and nowadays, driverless transport systems are already state-of-the-art on certain legs of transportation. This has given a push for the maritime industry to join the advancement. The case organisation, AAWA initiative, is a joint industry-academia research consortium with the objective of developing readiness for the first commercial autonomous solutions, exploiting state-of-the-art autonomous and remote technology. The initiative develops both autonomous and remote operation technology for navigation, machinery, and all on-board operating systems. The aim of this study is to develop a model with which to estimate and forecast the operational costs, and thus enable comparisons between manned and autonomous cargo vessels. The building process of the model is also described and discussed. Furthermore, the model’s aim is to track and identify the critical success factors of the chosen ship design, and to enable monitoring and tracking of the incurred operational costs as the life cycle of the vessel progresses. The study adopts the constructive research approach, as the aim is to develop a construct to meet the needs of a case organisation. Data has been collected through discussions and meeting with consortium members and researchers, as well as through written and internal communications material. The model itself is built using activity-based life cycle costing, which enables both realistic cost estimation and forecasting, as well as the identification of critical success factors due to the process-orientation adopted from activity-based costing and the statistical nature of Monte Carlo simulation techniques. As the model was able to meet the multiple aims set for it, and the case organisation was satisfied with it, it could be argued that activity-based life cycle costing is the method with which to conduct cost estimation and forecasting in the case of autonomous cargo vessels. The model was able to perform the cost analysis and forecasting, as well as to trace the critical success factors. Later on, it also enabled, albeit hypothetically, monitoring and tracking of the incurred costs. By collecting costs this way, it was argued that the activity-based LCC model is able facilitate learning from and continuous improvement of the autonomous vessel. As with the building process of the model, an individual approach was chosen, while still using the implementation and model building steps presented in existing literature. This was due to two factors: the nature of the model and – perhaps even more importantly – the nature of the case organisation. Furthermore, the loosely organised network structure means that knowing the case organisation and its aims is of great importance when conducting a constructive research.

Integrated visual perception architecture for robotic clothes perception and manipulation

This thesis proposes a generic visual perception architecture for robotic clothes perception and manipulation. This proposed architecture is fully integrated with a stereo vision system and a dual-arm robot and is able to perform a number of autonomous laundering tasks. Clothes perception and manipulation is a novel research topic in robotics and has experienced rapid development in recent years. Compared to the task of perceiving and manipulating rigid objects, clothes perception and manipulation poses a greater challenge. This can be attributed to two reasons: firstly, deformable clothing requires precise (high-acuity) visual perception and dexterous manipulation; secondly, as clothing approximates a non-rigid 2-manifold in 3-space, that can adopt a quasi-infinite configuration space, the potential variability in the appearance of clothing items makes them difficult to understand, identify uniquely, and interact with by machine. From an applications perspective, and as part of EU CloPeMa project, the integrated visual perception architecture refines a pre-existing clothing manipulation pipeline by completing pre-wash clothes (category) sorting (using single-shot or interactive perception for garment categorisation and manipulation) and post-wash dual-arm flattening. To the best of the author’s knowledge, as investigated in this thesis, the autonomous clothing perception and manipulation solutions presented here were first proposed and reported by the author. All of the reported robot demonstrations in this work follow a perception-manipulation method- ology where visual and tactile feedback (in the form of surface wrinkledness captured by the high accuracy depth sensor i.e. CloPeMa stereo head or the predictive confidence modelled by Gaussian Processing) serve as the halting criteria in the flattening and sorting tasks, respectively. From scientific perspective, the proposed visual perception architecture addresses the above challenges by parsing and grouping 3D clothing configurations hierarchically from low-level curvatures, through mid-level surface shape representations (providing topological descriptions and 3D texture representations), to high-level semantic structures and statistical descriptions. A range of visual features such as Shape Index, Surface Topologies Analysis and Local Binary Patterns have been adapted within this work to parse clothing surfaces and textures and several novel features have been devised, including B-Spline Patches with Locality-Constrained Linear coding, and Topology Spatial Distance to describe and quantify generic landmarks (wrinkles and folds). The essence of this proposed architecture comprises 3D generic surface parsing and interpretation, which is critical to underpinning a number of laundering tasks and has the potential to be extended to other rigid and non-rigid object perception and manipulation tasks. The experimental results presented in this thesis demonstrate that: firstly, the proposed grasp- ing approach achieves on-average 84.7% accuracy; secondly, the proposed flattening approach is able to flatten towels, t-shirts and pants (shorts) within 9 iterations on-average; thirdly, the proposed clothes recognition pipeline can recognise clothes categories from highly wrinkled configurations and advances the state-of-the-art by 36% in terms of classification accuracy, achieving an 83.2% true-positive classification rate when discriminating between five categories of clothes; finally the Gaussian Process based interactive perception approach exhibits a substantial improvement over single-shot perception. Accordingly, this thesis has advanced the state-of-the-art of robot clothes perception and manipulation.

Transition to Product Service Systems: methodology based on scenarios identification, modelling and evaluation

Part 4: Transition Towards Product-Service Systems

Report of the Stock Identification Methods Working Group (SIMWG) 2016

The Stock Identification Methods Working Group (SIMWG) worked by correspondence in 2016. The working group was chaired by Lisa Kerr (USA). The work plan for SIMWG in 2016 comprised four Terms of Reference (ToR), some of which are continuing goals for SIMWG: a ) Review recent advances in stock identification methods; b ) Build a reference database with updated information on known biological stocks for species of ICES interest; c ) Provide technical reviews and expert opinions on matters of stock identifica-tion, as requested by specific Working Groups and SCICOM; d ) Review and report on advances in mixed stock analysis, and assess their po-tential role in improving precision of stock assessment. ToR a) is an ongoing task of SIMWG in which we provide a comprehensive update on recent applications of stock identification techniques to ICES species of interest, summa-rize new approaches in stock identification, and novel combinations of existing applica-tions. ToR b) is a multi-annual ToR in which SIMWG has taking steps to build a reference data-base consisting of SIMWG reviews of issues of stock identity for ICES species. ToR c) is a key ongoing task by SIMWG in which we addresses specific requests by ICES working groups for technical advice on issues of stock identity. This year we provided advice on mackerel in the Northeast Atlantic as requested by WGWIDE. ToR d) is a multi-annual ToR that is focused on tracking developments in the application of mixed stock analysis and the integration of this information into assessment and management.