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Adaptive spatio-temporal graph learning for bus station profiling
No full textUnderstanding and managing public transportation systems require capturing complex spatio-temporal correlations within datasets. Existing studies often use predefined graphs in graph learning frameworks, neglecting shifted spatial and long-term temporal correlations, which are crucial in practical applications. To address these problems, we propose a novel bus station profiling framework to automatically infer the spatio-temporal correlations and capture the shifted spatial and long-term temporal correlations in the public transportation dataset. The proposed framework adopts and advances the graph learning structure through the following innovative ideas: (1) designing an adaptive graph learning mechanism to capture the interactions between spatio-temporal correlations rather than relying on pre-defined graphs, (2) modeling shifted correlation in shifted spatial graphs to learn fine-grained spatio-temporal features, and (3) employing self-attention mechanism to learn the long-term temporal correlations preserved in public transportation data. We conduct extensive experiments on three real-world datasets and exploit the learned profiles of stations for the station passenger flow prediction task. Experimental results demonstrate that the proposed framework outperforms all baselines under different settings and can produce meaningful bus station profiles. © 2024 held by the owner/author(s)
The length of fracture process zone deciphers variations of rock tensile strength
Get PDFTensile strength is one of the most critical design factors in many rock engineering projects. However, despite many available testing techniques, an accurate estimation of the true tensile strength of quasi-brittle rock-like materials is yet a controversial problem since it can vary by the shape and size of a test specimen, the adopted test method, and applied loading conditions. Different studies have tried to address this issue by providing (mainly empirical) laws for determining variations of rock tensile strength as a function of a particular test parameter such as specimen size. In this study, however, a new general approach is presented that can decipher the tensile strength variations of rock under various testing conditions. Using coupled Finite Fracture Mechanics (FFM), it is first proved that the length of the Fracture Process Zone (FPZ) can be determined with accuracy and ease using the energy criterion of coupled FFM. Then, the length of FPZ is used in the stress criterion of coupled FFM to determine rock tensile strength. The failure stress of a material is then proved to be mainly a function of the FPZ length following a power law originated from the Linear Elastic Fracture Mechanics (LEFM). The results assist in deciphering variations of rock tensile strength related to the sample size and test method. © 2024 The Author
Improved PWM Switching Scheme to Mitigate Power Loss and Switch Temperature of CHB Inverters
No full textPulsewidth modulation (PWM) techniques play a crucial role in determining the power quality of multilevel-inverter-based grid-tied solar photovoltaic (PV) fed systems. However, the existing PWM techniques suffer from the heat dissipation of the switches and power loss issues. In view of this concern, a new PWM technique is proposed to mitigate the junction temperature as well as the power loss of a cascaded H-bridge (CHB) inverter employed in a grid-tied solar PV system. Apart from the junction temperature of the power switch and power loss, different steady-state and dynamic responses of the CHB inverter are investigated using MATLAB/Simulink and PLECS software environments. Experimental results are also provided to support the simulation analysis
The narrative of a VET workforce shortage in Australia : reality, myth or opportunity?
No full textPurpose: The aim of the paper is to examine whether there really is a shortage of VET teachers, and if so, whether there are links to the salary offered and to the qualifications required. Design/methodology/approach: The paper uses three main approaches to examine the narrative of a shortage of VET teachers in Australia. Findings: There was no documented evidence of a VET teacher shortage, beyond a general perception of shortage in line with other occupations due to the post-COVID economic recovery. Salaries for VET teachers were found to compare well with other education occupations and other jobs in the economy. There was no evidence of the required qualifications deterring entry. The main concern appears to be whether VET can adequately train workers for other sectors in shortage. Research limitations/implications: The research did not include empirical survey work and suggests that this needs to be carried out urgently. Practical implications: The research provides evidence that will challenge current assumptions and help in the recruitment of VET teachers. Social implications: It argues for a recognition of the importance of the VET sector beyond its function of serving industry. Originality/value: It highlights ways to make VET teaching a more attractive proposition and to better promote its advantages. © 2023, Emerald Publishing Limited
Identification of high-performing soil groups in grazing lands using a multivariate analysis method
Get PDFUnderstanding and quantifying the complex relationships between soil properties and vegetation health is important for sustainable land management and optimising agricultural productivity. This study tested a spatial data-driven framework to identify the soil groups associated with pasture health using publicly available gridded soil attribute layers from Soil and Landscape Grid of Australia (SLGA) over two adjacent southeast Australian river catchments. Principal component analysis (PCA) followed by isocluster unsupervised classification was applied to seventeen SLGA soil attribute layers to identify dominant soil patterns, which showed good spatial agreement with the Enhanced Vegetation Index (EVI) data derived from Landsat-8 imagery. The soil class demonstrating the highest EVI values (HVR class) and the lowest EVI values (LVR class) were determined. A comparison of these classes with soil types defined in the New South Wales Soil Landscape maps confirmed that the HVR class is predominated by agriculturally productive, basalt-derived 'Ant Hill' soils. The cation exchange capacity (CEC) ranging fro
A novel multi-objective deep q-network: addressing immediate and delayed rewards in multi-objective q-learning
Get PDFCurrent multi-objective reinforcement learning (MORL) research often struggles to balance multiple objectives and manage the stability and performance of learning algorithms, especially in complex environments. To address this, we propose a new multi-objective deep Q-network (MO-DQN) framework that integrates linear scalarization in MORL. This framework has the following features: First, we provide immediate feedback by incorporating linear scalarization into reward processing. Compared with some complex multi-objective optimization methods, this approach is relatively easy to understand and implement, offering greater convenience for practical applications. Additionally, linear scalarization accelerates the learning process and enhances the algorithm's ability to dynamically adjust strategies. Second, we develop the Linear Scalarized Multi-objective Deep Q-Network (LSMO-DQN) under different reward mechanisms, improving MO-DQN's ability to balance multiple objectives effectively. Immediate reward strategies accelerate learning and enable rapid adjustments, benefiting dynamic environments. In contrast, delayed reward strategies help understand long-term action impacts and promote strategic decision-making. Experiments are conducted in two different multi-objective environments, where our proposed method slightly outperforms other techniques, indicating its robustness and adaptability. Specifically, LSMO-DQN achieves higher cumulative rewards and demonstrates improved stability across various reward structures. The findings suggest that integrating linear scalarization in reward processing not only enhances learning performance but also provides a more straightforward approach to managing the trade-offs in multi-objective settings. These results highlight the potential of LSMO-DQN to improve learning performance, particularly in scenarios with data imbalances. © 2024 The Authors
A mixed-method evaluation of peer-led education about attitudes towards consumers' recovery among Mental Health Nurses working in acute inpatient psychiatric units
Get PDFDespite integrating the recovery model of care in mental health, mental health professionals still have pessimistic attitudes towards the recovery of people with mental illness. Positive attitudes towards recovery are essential components to integrate recovery-oriented practices in all areas of mental health. Evidence shows that education and training are effective while emphasising the importance of consumer-based interventions to enhance recovery attitudes. This study aimed to evaluate the effectiveness of peer-led education about recovery attitudes towards people with mental illness among Mental Health Nurses working in acute inpatient settings. The methodology used was a sequential explanatory mixed method with pre- and post-test design involving three phases. Phase 1: survey (n = 103), phase 2: post-test survey immediate (n = 17) and follow-up (n = 11) and phase 3: in-depth interviews (n = 12). The results show that Mental Health Nurses have positive recovery attitudes with some room for improvement. Most participants agreed with all items of the Recovery Attitudes Questionnaire. However, the participants had various views on the relationship between faith and recovery. The peer-led education significantly improved RAQ items 1, 2, 3, 4 and 6 statistically. Furthermore, peer-led education effectively enhanced recovery attitudes immediately after the intervention and helped to maintain sustainable attitudes 3 months later. A qualitative exploration of recovery attitudes revealed three main themes: participants' reflections, recovery hurdles and interpersonal relationships. © 2024 The Authors. International Journal of Mental Health Nursing published by John Wiley & Sons Australia, Ltd
Statistical reappraisal of the wax and mercury methods for shrinkage limit determinations of fine-grained soils
Get PDFBecause of the hazards associated with handling mercury, most standards organizations have withdrawn the conventional mercury (displacement) method (MM) for shrinkage limit (SL) determination of fine-grained soils. Despite attempts to substantiate the wax (coating) method (WM), which is presently the only standardized MM-testing alternative, the geotechnical community remains somewhat hesitant of its adoption in routine practice. To encourage more widespread use of WM-testing, this study re-examines the level of agreement between the MM- and WM-deduced SL parameters (i.e., SLMM and SLWM, respectively). This was achieved by performing comprehensive statistical analyses on the largest and most diverse database of its kind, to date, entailing SLMM:SLWM measurements for 168 different fine-grained soils having wide ranges of plasticity characteristics (i.e., liquid limit = 31.6–362.0%, plasticity index = 8.2–318.0% and SLMM = 7.1–42.0%). Furthermore, an attempt was made to evaluate the SLWM (in lieu of the SLMM) parameter for performing preliminary soil expansivity assessments using existing SLMM-based classification approaches. It was demonstrated that the MM and WM methods do not produce identical SL values for a given fine-grained soil under similar testing conditions, with their discrepancy being systematic and hence likely arising from the differences between the materials (mercury versus wax) and methodologies involved in performing these tests. New SLW
Advanced voltage balancing discontinuous PWM technique for solar PV fed grid-tied NPC inverters
No full textMultilevel inverters (MLIs) have significantly improved the overall performance, dependability and efficiency of the renewable energy system. Moreover, these can be easily integrated with the superconducting magnetic energy storage (SMES) systems. Maintaining the power qualities of these MLIs is always marked as a major research concern which can be heavily impacted by the pulse width modulation (PWM) strategies. An improved voltage balancing discontinuous PWM (DPWM) scheme is suggested in this work for the single-phase grid-tied 5-level neutral point clamped (NPC) inverter, which can significantly mitigate the fluctuation of the dc-link capacitor voltages as well as the switching losses of the power IGBTs. The reduction in switching losses will give lower thermal stress to the power devices. The proposed DPWM scheme is compared with other existing DPWM schemes for proving its effectiveness. The simulation of the entire system is performed by using MATLAB Simulink and PLECS simulation platform. A lower scale prototype is also constructed in the laboratory. © 2002-2011 IEEE