4,235 research outputs found
Architecture Smells vs. Concurrency Bugs: an Exploratory Study and Negative Results
Technical debt occurs in many different forms across software artifacts. One
such form is connected to software architectures where debt emerges in the form
of structural anti-patterns across architecture elements, namely, architecture
smells. As defined in the literature, ``Architecture smells are recurrent
architectural decisions that negatively impact internal system quality", thus
increasing technical debt. In this paper, we aim at exploring whether there
exist manifestations of architectural technical debt beyond decreased code or
architectural quality, namely, whether there is a relation between architecture
smells (which primarily reflect structural characteristics) and the occurrence
of concurrency bugs (which primarily manifest at runtime). We study 125
releases of 5 large data-intensive software systems to reveal that (1) several
architecture smells may in fact indicate the presence of concurrency problems
likely to manifest at runtime but (2) smells are not correlated with
concurrency in general -- rather, for specific concurrency bugs they must be
combined with an accompanying articulation of specific project characteristics
such as project distribution. As an example, a cyclic dependency could be
present in the code, but the specific execution-flow could be never executed at
runtime
Optimising acoustic cavitation for industrial application
The ultrasonic horn is one of the most commonly used acoustic devices in laboratories and industry. For its efficient application to cavitation mediated process, the cavitation generated at its tip as a function of its tip-vibration amplitudes still needed to be studied in detail. High-speed imaging and acoustic detection are used to investigate the cavitation generated at the tip of an ultrasonic horn, operating at a fundamental frequency, f0, of 20 kHz. Tip-vibration amplitudes are sampled at fine increments across the range of input powers available. The primary bubble cluster under the tip is found to undergo subharmonic periodic collapse, with concurrent shock wave emission, at frequencies of f0/m, with m increasing through integer values with increasing tip-vibration amplitude. The contribution of periodic shock waves to the noise spectra of the acoustic emissions is confirmed. Transitional input powers for which the value of m is indistinct, and shock wave emission irregular and inconsistent, are identified through Vrms of the acoustic detector output. For cavitation applications mediated by bubble collapse, sonications at transitional powers may lead to inefficient processing. The ultrasonic horn is also deployed to investigate the role of shock waves in the fragmentation of intermetallic crystals, nominally for ultrasonic treatment of Aluminium melt, and in a novel two-horn configuration for potential cavitation enhancement effects. An experiment investigating nitrogen fixation via cavitation generated by focused ultrasound exposures is also described. Vrms from the acoustic detector is again used to quantify the acoustic emissions for comparison to the sonochemical nitrite yield and for optimisation of sonication protocols at constant input energy. The findings revealed that the acoustic cavitation could be enhanced at constant input energy through optimisation of the pulse duration and pulse interval. Anomalous results may be due to inadequate assessment for the nitrate generated. The studies presented in this thesis have illustrated means of improving the cavitation efficiency of the used acoustic devices, which may be important to some selected industrial processes
The applied psychology of addictive orientations : studies in a 12-step treatment context.
The clinical data for the studies was collected at The PROMIS Recovery Centre, a Minnesota Model treatmentc entre for addictions,w hich encouragesth e membership and use of the 12 step Anonymous Fellowships, and is abstinence based. The area of addiction is contextualised in a review chapter which focuses on research relating to the phenomenon of cross addiction. A study examining the concept of "addictive orientations" in male and female addicts is described, which develops a study conductedb y StephensonM, aggi, Lefever, & Morojele (1995). This presents study found a four factor solution which appeared to be subdivisions of the previously found Hedonism and Nurturance factors. Self orientated nurturance (both food dimensions, shopping and caffeine), Other orientated nurturance (both compulsive helping dimensions and work), Sensation seeking hedonism (Drugs, prescription drugs, nicotine and marginally alcohol), and Power related hedonism (Both relationship dimensions, sex and gambling. This concept of "addictive orientations" is further explored in a non-clinical population, where again a four factor solution was found, very similar to that in the clinical population. This was thought to indicate that in terms of addictive orientation a pattern already exists in this non-clinical population and that consideration should be given to why this is the case. These orientations are examined in terms of gender differences. It is suggested that the differences between genders reflect power-related role relationships between the sexes. In order to further elaborate the significance and meaning behind these orientations, the next two chapters look at the contribution of personality variables and how addictive orientations relate to psychiatric symptomatology. Personality variables were differentially, and to a considerable extent predictably involved with the four factors for both males and females.Conscientiousness as positively associated with "Other orientated Nurturance" and negatively associated with "Sensation seeking hedonism" (particularly for men). Neuroticism had a particularly strong association with the "Self orientated Nurturance" factor in the female population. More than twice the symptomatology variance was explained by the factor scores for females than it was for males. The most important factorial predictors for psychiatric symptomatology were the "Power related hedonism" factor for males, and "Self oriented nurturance" for females. The results are discussed from theoretical and treatment perspectives
The role of language and sensorimotor information in memory for concepts
The linguistic-simulation approach to conceptual representations has been investigated for some time, but the role of language and sensorimotor information in memory for objects and words, both short- and long-term, has not been examined in detail. In the present thesis, I look at the interplay of sensorimotor and linguistic information in conceptual knowledge and examine which aspects of concepts are represented in memory tasks. I also aim to establish the role of consciously accessing conceptual information in word recognition and memory. The thesis includes three self-contained papers which show that the conceptual system relies on linguistic or sensorimotor information according to task demands. In the paper in Chapter 4, I examined the linguistic bootstrapping hypothesis, which postulates that linguistic labels can serve as placeholders for complex sensorimotor representations. I tested the capacity of working memory for object concepts using an articulatory suppression task to block access to language. I found that working memory capacity for contextually related object concepts when relying on sensorimotor information is higher than the traditionally reported capacity of 3-4 for simple shapes or colours. Additionally, when linguistic labels are available to deputise for complex sensorimotor information, the capacity further increases by up to two object concepts. In Chapters 5 and 6, I examined the content of conceptual information stored in long-term memory, and the role of sensorimotor simulation and consciously available information in word recognition and word memory. The studies revealed that consciously generated imagery is not reliably measured, and moreover, it does not contribute to word recognition in a consistent manner. Some of the effects of imageability found in the literature can be explained or subsumed by sensorimotor information, which is not fully available through conscious awareness. However, conscious imagery may be a useful strategy to support word memory when trying to explicitly remember words. The thesis demonstrates that both linguistic labels and sensorimotor information contribute to memory for concepts. The way a concept is represented in different tasks varies depending on task demands. Linguistic information is used to circumvent resource capacity limits, while sensorimotor information guides behaviour by providing more detailed information about the meaning of concepts, and our previous experience with them
Stacked LSTM Sequence-to-Sequence Autoencoder with Feature Selection for Daily Solar Radiation Prediction: A Review and New Modeling Results
We review the latest modeling techniques and propose new hybrid SAELSTM framework based on Deep Learning (DL) to construct prediction intervals for daily Global Solar Radiation (GSR) using the Manta Ray Foraging Optimization (MRFO) feature selection to select model parameters. Features are employed as potential inputs for Long Short-Term Memory and a seq2seq SAELSTM autoencoder Deep Learning (DL) system in the final GSR prediction. Six solar energy farms in Queensland, Australia are considered to evaluate the method with predictors from Global Climate Models and ground-based observation. Comparisons are carried out among DL models (i.e., Deep Neural Network) and conventional Machine Learning algorithms (i.e., Gradient Boosting Regression, Random Forest Regression, Extremely Randomized Trees, and Adaptive Boosting Regression). The hyperparameters are deduced with grid search, and simulations demonstrate that the DL hybrid SAELSTM model is accurate compared with the other models as well as the persistence methods. The SAELSTM model obtains quality solar energy prediction intervals with high coverage probability and low interval errors. The review and new modelling results utilising an autoencoder deep learning method show that our approach is acceptable to predict solar radiation, and therefore is useful in solar energy monitoring systems to capture the stochastic variations in solar power generation due to cloud cover, aerosols, ozone changes, and other atmospheric attenuation factors
Studies of phase and strength in high pressure vanadium
High temperature and pressure states of matter are ubiquitous in nature. It
is therefore of the upmost importance that they are understood. Extreme
pressures can be obtained using both static and dynamic compression. Static
compression using a diamond anvil cell (DAC) can reach pressures into the multi-100 GPa range and, combined with resistive and laser heating techniques, can
simultaneously access high-temperature regimes. Dynamic compression using
either impactors or lasers can be employed to simultaneously generate high
temperature-pressure states into the TPa regime. The differing pressure and
temperature states accessed make static and dynamic compression methods
ideal for complimentary studies on a material’s behaviour. To probe material
conditions under extreme conditions x-ray diffraction is a powerful tool, allowing
the different structures adopted by samples to be studied directly.
In this thesis in situ x-ray diffraction was combined with static and dynamic
compression techniques to probe the structural behaviour of vanadium (V),
the high-pressure behaviour of which is unique amongst the elements. At 120
GPa, V has one of the highest known superconducting transition temperatures
in the elements, Tc=17.2 K. Subsequent lattice dynamic calculations aimed at
understanding the high Tc noted dramatic softening of a transverse acoustic
mode, suggestive of a structural phase transition. A bcc-rhombohedral structural
transition has been reported by x-ray diffraction studies using DACs at 30-70
GPa, and the same transition has also been reported in several computational
studies. These latter studies predict a transition to a second rhombohedral phase
at 120 GPa, and a re-entrant transition to the bcc phase at 280 GPa. Neither
of these higher-pressure phases have been observed experimentally. Dynamic
compression studies of V have reported evidence of a phase transition starting at
32 GPa and completing at 60 GPa, but no x-ray diffraction data were collected. Despite near universal agreement on there being a bcc-rhombohedral phase transition in V between 30 and 70 GPa, the experimental evidence is surprisingly
weak. Indeed, close analysis of the published diffraction data reveals that they do
not fit a rhombohedral structure, thereby inspiring the current study of V using
both static and dynamic compression techniques. For direct comparison with previous studies, V was compressed with no pressure
transmitting medium (PTM) to 139 GPa and with a mineral oil PTM to 118 GPa.
The diffraction data from these non-hydrostatic experiments were completely
consistent with previous studies, showing splitting of the bcc peaks starting at 45
GPa. However, these splittings could not be fitted by the reported rhombohedral
structure. V samples were also compressed in He to 154 GPa, reproducing
previous quasi-hydrostatic studies. The peaks of the bcc phase were observed to
start splitting at pressures as low at 20 GPa, but again a rhombohedral structure
was unable fit the diffraction profiles from the high-pressure phase. Difficulties in studying the bcc-rhombohedral transition arise from an inherent
limitation of the powder-diffraction method, that is the overlap of peaks with
similar d-spacings. This was overcome by making studies of [001]-oriented single
crystals of V compressed in a mineral oil PTM to 118 GPa. The bcc peaks were
found to split at 40 GPa, and, in contrast to the powder studies, the high-pressure
phase could be fitted by a rhombohedral structure. The pressure dependence of
the rhombohedral angle is suggestive of two different rhombohedral phases, but
the distortions are much smaller than those predicted by theory. The excellent
fit of the rhombohedral phase to the single crystal data when compared with the
powder data suggests that texture of the powdered sample, which would not be
present in the single crystal may cause the misfits to the reported structures. For comparison with the DAC studies, shock compression studies were carried
out on V foils up to 180 GPa along the principal Hugoniot. The data collected
below 40 GPa were well fitted by the bcc structure but above that pressure
a rhombohedral structure gave a better fit. The fit was much better than
that obtained with statically compressed powder samples, and the rhombohedral
structure agrees with that obtained from the single-crystal data. For comparison
with the shock compression study, high-temperature static compression was
conducted using a resistive heating set up and a KCL PTM. The rhombohedral
transition was seen to occur at higher pressures under these conditions, with the
fitted rhombohedral lattice appearing more bcc-like, suggesting the rhombohedral
transition was suppressed by high temperatures
Entrepreneurial Behaviour in Informal Economy: A Case Study on Street Food Entrepreneurship in Dhaka, Bangladesh
This research investigates entrepreneurial behaviour of street food sellers operating within the informal food industry in Dhaka, Bangladesh. The aim of the research is to explore whether street food entrepreneurs are behaving productively, unproductively or destructively. Thirteen semi-structured interviews are conducted with government officials, policymakers, academics, food adulteration researchers, nutritionists and NGO representatives, providing insight into the food adulteration phenomenon, with particular attention given to the sector selling adulterated food to children. The research uses grounded theory in analysing qualitative data and finds that food safety concerns are more alarming within the informal street food industry. Following this, data is collected from 250 questionnaires with street food retailers. Chi-square tests are conducted to investigate statistical relationships between variables identified at the qualitative phase of the research. Findings reveal that entrepreneurs from the informal street food industry are generally more unproductive in their behaviour. In addition, the research finds that a number of the factors that influence the behaviour of street food retailers from the informal food industry are not within their control. An ineffective regulatory framework, concerns with education, knowledge and awareness of safe food among entrepreneurs are the main factors influencing entrepreneurs to behave unproductively. This research did not find any evidence suggesting destructive entrepreneurial behaviour. Theoretical and practical implications of the research findings are discussed, and the research recommends the regulatory body to develop a simple transition process for entrepreneurs from the informal street food industry to become formal, irrespective of their size and age of business
A Reinforcement Learning Quality of Service Negotiation Framework For IoT Middleware
The Internet of Things (IoT) ecosystem is characterised by heterogeneous devices dynamically interacting with each other to perform a specific task, often without human intervention. This interaction typically occurs in a service-oriented manner and is facilitated by an IoT middleware. The service provision paradigm enables the functionalities of IoT devices to be provided as IoT services to perform actuation tasks in critical-safety systems such as autonomous, connected vehicle system and industrial control systems. As IoT systems are increasingly deployed into an environment characterised by continuous changes and uncertainties, there have been growing concerns on how to resolve the Quality of Service (QoS) contentions between heterogeneous devices with conflicting preferences to guarantee the execution of mission-critical actuation tasks. With IoT devices with different QoS constraints as IoT service providers spontaneously interacts with IoT service consumers with varied QoS requirements, it becomes essential to find the best way to establish and manage the QoS agreement in the middleware as a compromise in the QoS could lead to negative consequences. This thesis presents a QoS negotiation framework, IoTQoSystem, for IoT service-oriented middleware. The QoS framework is underpinned by a negotiation process that is modelled as a Markov Decision Process (MDP). A model-based Reinforcement Learning negotiation strategy is proposed for generating an acceptable QoS solution in a dynamic, multilateral and multi-parameter scenarios. A microservice-oriented negotiation architecture is developed that combines negotiation, monitoring and forecasting to provide a self-managing mechanism for ensuring the successful execution of actuation tasks in an IoT environment. Using a case study, the developed QoS negotiation framework was evaluated using real-world data sets with different negotiation scenarios to illustrate its scalability, reliability and performance
Raphtory: Modelling, Maintenance and Analysis of Distributed Temporal Graphs.
PhD ThesesTemporal graphs capture the development of relationships within data throughout time. This
model ts naturally within a streaming architecture, where new events can be inserted directly
into the graph upon arrival from a data source and be compared to related entities or historical
state. However, the majority of graph processing systems only consider traditional graph analysis
on static data, whilst those which do expand past this often only support batched updating and
delta analysis across graph snapshots. In this work we de ne a temporal property graph model
and the semantics for updating it in both a distributed and non-distributed context. We have
built Raphtory, a distributed temporal graph analytics platform which maintains the full graph
history in memory, leveraging the de ned update semantics to insert streamed events directly into
the model without batching or centralised ordering. In parallel with the ingestion, traditional
and time-aware analytics may be performed on the most up-to-date version of the graph, as
well as any point throughout its history. The depth of history viewed from the perspective of
a time point may also be varied to explore both short and long term patterns within the data.
Through this we extract novel insights over a variety of use cases, including phenomena never
seen before in social networks. Finally, we demonstrate Raphtory's ability to scale both vertically
and horizontally, handling consistent throughput in excess of 100,000 updates a second alongside
the ingestion and maintenance of graphs built from billions of events
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