ORCID Profile
0000-0001-9151-4469
Current Organisations
Federation University Australia
,
University of Notre Dame Australia
,
Deakin University
,
Griffith University
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Publisher: Copernicus GmbH
Date: 15-05-2023
DOI: 10.5194/EGUSPHERE-EGU23-4804
Abstract: Water penetration, changes in the groundwater level and moisture content changes can affect the physical and chemical properties of coal in an open pit mine. Water levels in open coal pit mines can vary throughout the year, resulting in a number of wet and dry cycles for brown coal. Wet and dry cycles occurring throughout the year can affect the mechanical strength of the stone's microstructure and macroscopic structure. Loss of strength can have severe negative impacts if such rock is integral component in landform design. Until now, no research has been conducted on the effects of wet and dry loading cycles on brown coal. This study investigates the effect of wet and dry cycles on brown coal's strength by conducting a series of unconfined compressive strength (UCS) laboratory tests. For this purpose, nine laboratory s les with dimensions of 38 x 76 cm were prepared. S les were placed inside distilled water chambers in a temperature-controlled environment. Afterwards, the s les were subjected to unconfined compressive strength (UCS) tests following 0, and 3 cycles of wet and dry conditions. The results of the UCS test show that as the number of wetting and drying cycles increased, the UCS of the s les decreased from 2150 to 330 kPa after three cycles of wetting and drying. In addition, the results indicate that the elastic modulus of brown coal has decreased from 10500 to 1200 kPa. Also, the Poisson ratio decreased from 0.34 to 0.27. This study confirms the importance of paying attention to the wet and dry cycles in brown coal mines.
Publisher: Springer Science and Business Media LLC
Date: 22-05-2023
DOI: 10.1007/S40279-023-01862-9
Abstract: Cancer-related pain is common and undertreated. Exercise is known to have a pain-relieving effect in non-cancer pain. This systematic review aimed to evaluate (1) the effect of exercise on cancer-related pain in all cancers, and (2) whether the effect of exercise differed according to exercise mode, degree of supervision, intervention duration and timing (during or after cancer treatment), pain types, measurement tool and cancer type. Electronic searches were undertaken in six databases to identify exercise studies evaluating pain in people with cancer, published prior to 11 January 2023. All stages of screening and data extraction were conducted independently by two authors. The Cochrane risk of bias tool for randomised trials (RoB 2) was used and overall strength of evidence was assessed using the GRADE approach. Meta-analyses were performed overall and by study design, exercise intervention and pain characteristics. In total, 71 studies reported in 74 papers were eligible for inclusion. The overall meta-analysis included 5877 participants and showed reductions in pain favouring exercise (standardised mean difference − 0.45 95% confidence interval − 0.62, − 0.28). For most ( 82%) of the subgroup analyses, the direction of effect favoured exercise compared with usual care, with effect sizes ranging from small to large (median effect size − 0.35 range − 0.03 to − 1.17). The overall strength of evidence for the effect of exercise on cancer-related pain was very low. The findings provide support that exercise participation does not worsen cancer-related pain and that it may be beneficial. Better pain categorisation and inclusion of more erse cancer populations in future research would improve understanding of the extent of benefit and to whom. CRD42021266826.
Publisher: MDPI AG
Date: 29-03-2023
DOI: 10.3390/APP13074363
Abstract: This study investigates the effects of sand particle shape, in terms of roundness, sphericity and regularity, on the d ing ratio of a dry sand material. Twelve different cyclic simple shear testing scenarios were considered and applied using vertical stresses of 50, 150 and 250 kPa and cyclic stress ratios (CSR) of 0.2, 0.3, 0.4 and 0.5 in both constant- and controlled-stress modes. Each testing scenario involved five tests, using the same sand that was reconstructed from its previous cyclic test. On completion of the cyclic tests, corresponding hysteresis loops were established to determine the d ing ratio. The results indicated that the minimum and maximum d ing ratios for this sand material were 6.9 and 25.5, respectively. It was observed that the shape of the sand particles changed during cyclic loading, becoming progressively more rounded and spherical with an increasing number of loading cycles, thereby resulting in an increase in the d ing ratio. The second part of this investigation involved the development of artificial intelligence models, namely an artificial neural network (ANN) and a support vector machine (SVM), to predict the effects of sand particle shape on the d ing ratio. The proposed ANN and SVM models were found to be effective in predicting the d ing ratio as a function of the particle shape descriptors (i.e., roundness, sphericity and regularity), vertical stress, CSR and number of loading cycles. Finally, a sensitivity analysis was conducted to identify the importance of the input variables the vertical stress and regularity were, respectively, ranked as first and second in terms of importance, while the CSR was found to be the least important parameter.
Publisher: Informa UK Limited
Date: 28-05-2023
Publisher: Elsevier BV
Date: 08-2023
Publisher: MDPI AG
Date: 07-07-2023
DOI: 10.3390/GEOTECHNICS3030035
Abstract: The thermal conductivity of materials is a crucial property with erse applications, particularly in engineering. Understanding soil thermal conductivity is crucial for designing efficient geothermal systems, predicting soil temperatures, and assessing soil contamination. This paper aimed to predict quartz sand thermal conductivity by using four mathematical models: multiple linear regression (MLR), artificial neural network (ANN), classification and regression random forest (CRRF), and genetic programming (GP). A grey-box AI method, GP, was used for the first time in this topic. Seven inputs affecting thermal conductivity were evaluated in the study, including sand porosity, degree of saturation, coefficient of uniformity, coefficient of curvature, mean particle size, and minimum and maximum void ratios. In predicting thermal conductivity, the MLR model performed poorly, with a coefficient of determination R2 = 0.737 and a mean absolute error MAE = 0.300. Both ANN models using the Levenberg–Marquardt algorithm and the Bayesian Regularization (BR) algorithm outperformed the MLR model with an accuracy of R2 = 0.916 and an error of MAE = 0.151. In addition, the CRRF model had the best accuracy of R2 = 0.993 and MAE = 0.045. In addition, GP showed acceptable performance in predicting sand thermal conductivity. The R2 and MAE values of GP were 0.986 and 0.063, respectively. This paper presents the best GP equation for evaluating other databases. Additionally, the porosity and saturation of the sand were found to have the greatest impact on the model results, while coefficients of curvature and uniformity had the least influence. Overall, the results of this study demonstrate that grey-box artificial intelligence models can be used to accurately predict quartz sand thermal conductivity.
Publisher: Elsevier BV
Date: 02-2023
Publisher: Elsevier BV
Date: 09-2021
Publisher: Springer Science and Business Media LLC
Date: 19-06-2023
Publisher: Wiley
Date: 21-10-2023
DOI: 10.1111/JDV.18649
Publisher: MDPI AG
Date: 28-06-2023
DOI: 10.3390/GEOSCIENCES13070197
Abstract: This study aimed to examine the shear strength characteristics of sand–granular rubber mixtures in direct shear tests. Two different sizes of rubber and one of sand were used in the experiment, with the sand being mixed with various percentages of rubber (0%, 10%, 20%, 30%, and 50%). The mixtures were prepared at three different densities (loose, slightly dense, and dense), and shear stress was tested at four normal stresses (30, 55, 105, and 200 kPa). The results of 80 direct shear tests were used to calculate the peak and residual internal friction angles of the mixtures, and it was found that the normal stress had a significant effect on the internal friction angle, with an increase in normal stress leading to a decrease in the internal friction angle. These results indicated that the Mohr–Coulomb theory, which applies to rigid particles only, is not applicable in sand–rubber mixtures, where stiff particles (sand) and soft particles (rubber) are mixed. The shear strength of the mixtures was also influenced by multiple factors, including particle morphology (size ratio, shape, and gradation), mixture density, and normal stress. For the first time in the literature, genetic programming, classification and regression random forests, and multiple linear regression were used to predict the peak and residual internal friction angles. The genetic programming resulted in the creation of two new equations based on mixture unit weight, normal stress, and rubber content. Both artificial intelligence models were found to be capable of accurately predicting the peak and residual internal friction angles of sand–rubber mixtures.
Publisher: Elsevier BV
Date: 10-2023
Publisher: Wiley
Date: 20-12-2022
DOI: 10.1111/AJD.13776
Publisher: Wiley
Date: 20-02-2023
DOI: 10.1111/AJD.14009
Publisher: Wiley
Date: 27-03-2023
DOI: 10.1111/AJD.14025
Abstract: Academic dermatologists in Australia and New Zealand provide high‐quality and meaningful contributions to the understanding of disease and therapeutic translational research. Concerns have been raised by the Australian Medical Association regarding the decline of clinical academics in Australia as a whole, however, such trends in scholarly output have not previously been analysed for Australasian dermatologists. A bibliometric analysis of dermatologists in Australia and New Zealand was conducted in January and February 2023. Available Scopus profiles for all dermatologists were used to measure lifetime H index, scholarly output, citation counts and field‐weighted citation impact (FWCI) in the last 5 years (2017–2022). Trends in output over time were measured using non‐parametric tests. Differences in output between subgroups stratified by gender and academic leadership positions (associate professor or professor) were measured using Wilcoxon rank‐sum and one‐way ANOVA tests. The scholarly output of recent College graduates was also analysed as a subgroup, comparing the same bibliographic variables in the 5 years preceding and 5 years following awarding of their fellowships. From the 463 practising dermatologists in Australia and New Zealand, 372 (80%) were successfully matched to Scopus researcher profiles. Of these dermatologists, 167 were male (45%) and 205 (55%) were female, and 31 (8%) held academic leadership positions. Most dermatologists (67%) published at least one paper in the last 5 years. The median lifetime H index was 4, and between 2017 and 2022 median scholarly output was 3, the median citations were 14 and the median FWCI was 0.64. There was a non‐significant trend towards fewer publications per year, however, citation count and FWCI decreased significantly. By subgroups, female dermatologists published significantly more papers between 2017 and 2022, and other bibliographic variables were comparable to male dermatologists. However, women were underrepresented in positions of academic leadership—comprising only 32% of this cohort despite representing 55% of dermatologists. Professors were also significantly more likely to have higher bibliographic outcomes than associate professors. Finally, analysis of recent College graduates highlighted a significant decline in bibliometric outcomes pre‐ and post‐fellowship. Overall, our analysis identifies a trend towards decreased research output by dermatologists in Australia and New Zealand in the last 5 years. Strategies to support dermatologists in research endeavours, particularly women and recent graduates, will be essential in maintaining strong scholarly output among Australasian dermatologists and thereby sustaining optimal evidence‐based patient care.
Publisher: MDPI AG
Date: 25-09-2023
DOI: 10.3390/A16100456
Publisher: Wiley
Date: 17-08-2021
Publisher: Springer Science and Business Media LLC
Date: 29-03-2023
DOI: 10.1007/S11912-023-01385-6
Abstract: In the preceding decade, the management of metastatic cutaneous melanoma has been revolutionised with the development of highly effective therapies including immune checkpoint inhibitors (specifically CTLA-4 and PD-1 inhibitors) and targeted therapies (BRAF and MEK inhibitors). The role of chemotherapy in the contemporary management of melanoma is undefined. Extended analyses highlight substantially improved 5-year survival rates of approximately 50% in patients with metastatic melanoma treated with first-line therapies. However, most patients will progress on these first-line treatments. Sequencing of chemotherapy following failure of targeted and immunotherapies is associated with low objective response rates and short progression-free survival, and thus, meaningful benefits to patients are minimal. Chemotherapy has limited utility in the contemporary management of cutaneous melanoma (with a few exceptions, discussed herein) and should not be the standard treatment sequence following failure of first-line therapies. Instead, enrolment onto clinical trials should be standard-of-care in these patients.
Publisher: Ovid Technologies (Wolters Kluwer Health)
Date: 18-05-2023
DOI: 10.1097/CMR.0000000000000900
Abstract: Metastatic uveal melanoma (mUM) has historically been associated with short survival and limited effective treatments. Immune checkpoint inhibitors (ICIs) have been trialed in mUM however, robust conclusions regarding their efficacy are difficult to draw given small study sizes and heterogeneous patient populations. Five databases were searched using a combination of ‘ICI’ and ‘mUM’ headings, and data on patient demographics, objective response rate (ORR), overall survival (OS) and progression-free survival (PFS) were extracted. Pooled ORR was calculated using a random effects model and the inverse variance method. Available Kaplan–Meier OS and PFS curves were used to construct summary OS and PFS plots, from which median values were derived. Pooled ORR was 9.2% overall (95% CI 7.2–11.8) [4.1% for anti-CTLA4 (95% CI 2.1–7.7), 7.1% for anti-PD(L)1 (95% CI 4.5–10.9) and 13.5% for anti-CTLA4 plus anti-PD1 (95% CI 10.0–18.0)]. Median OS was 11.5 months overall (95% CI 9.5–13.8) [8.0 months for anti-CTLA4 (95% CI 5.5–9.9), 11.7 months for anti-PD(L)1 (95% CI 9.0–14.0) and 16.0 months for ipilimumab plus anti-PD1 (95% CI 11.5–17.7) ( P 0.001)]. Median PFS was 3.0 months overall (95% CI 2.9–3.1). ICIs have limited efficacy in mUM and a recommendation for their use must consider the balance of benefit and risk for in idual patients if no other options are available. Further biomarker profiling studies may be helpful in assessing which patients will benefit from ICIs, in particular the addition of ipilimumab to anti-PD1 therapy.
Publisher: Springer Science and Business Media LLC
Date: 09-04-2023
Publisher: Elsevier BV
Date: 10-2023
Publisher: Wiley
Date: 12-07-2022
DOI: 10.1111/JDV.18403
Publisher: Elsevier BV
Date: 02-2023
Publisher: MDPI AG
Date: 14-04-2023
DOI: 10.3390/APP13084934
Abstract: Alum sludge is a byproduct of water treatment plants, and its use as a soil stabilizer has gained increasing attention due to its economic and environmental benefits. Its application has been shown to improve the strength and stability of soil, making it suitable for various engineering applications. However, to go beyond just measuring the effects of alum sludge as a soil stabilizer, this study investigates the potential of artificial intelligence (AI) methods for predicting the California bearing ratio (CBR) of soils stabilized with alum sludge. Three AI methods, including two black box methods (artificial neural network and support vector machines) and one grey box method (genetic programming), were used to predict CBR, based on a database with nine input parameters. The results demonstrate the effectiveness of AI methods in predicting CBR with good accuracy (R2 values ranging from 0.94 to 0.99 and MAE values ranging from 0.30 to 0.51). Moreover, a novel approach, using genetic programming, produced an equation that accurately estimated CBR, incorporating seven inputs. The analysis of parameter sensitivity and importance, revealed that the number of hammer blows for compaction was the most important parameter, while the parameters for maximum dry density of soil and mixture were the least important. This study highlights the potential of AI methods as a useful tool for predicting the performance of alum sludge as a soil stabilizer.
Publisher: Wiley
Date: 20-10-2021
DOI: 10.1002/SKI2.71
Abstract: Sarcoidosis is a non‐infective granulomatous disorder of unknown aetiology, with cutaneous involvement affecting up to 30% of patients. Drug‐induced sarcoidosis has been reported secondary to modern melanoma therapies including immune‐checkpoint inhibitors and first generation BRAF inhibitors such as vemurafenib and dabrafenib. Herein, we report a case of cutaneous micropapular sarcoidosis that first developed on immune‐checkpoint inhibition with ipilimumab and nivolumab for metastatic melanoma, which was exacerbated and further complicated by pityriasis rubra pilaris‐like palmar plaques upon transition to a next‐generation BRAF‐dimerisation inhibitor. Both the micropapular eruption and palmar plaques rapidly resolved after cessation of the novel BRAF‐inhibitor and concurrent commencement of hydroxychloroquine. It is unclear how inhibition of BRAF‐dimerisation results in granuloma formation, though upregulation of T H 1/T H 17 T‐cells and impairment of T‐reg cells may be responsible. Clinicians should be aware of the potential for exacerbation of sarcoidosis when transitioning from immune‐checkpoint inhibitors to these novel BRAF‐dimerisation inhibitors, particularly as their uptake in treating cancers increases beyond clinical trials. Further studies are required to assess whether these next‐generation agents can trigger sarcoidosis de‐novo, or simply exacerbate pre‐existing sarcoidosis.
Publisher: Informa UK Limited
Date: 21-10-2022
Publisher: Wiley
Date: 30-07-2022
DOI: 10.1002/RCR2.1010
Abstract: Disseminated primary varicella infection can carry risks of significant morbidity and mortality particularly in immunocompromised populations. Routine, funded childhood vaccination against varicella has significantly reduced associated hospitalization and deaths, however, uptake and efficacy among adults is unknown. We present a case of disseminated primary varicella infection (including rash, pneumonitis, hepatitis and thrombocytopenia) in an immunocompetent patient on long term inhaled corticosteroids for asthma. This case highlights potential risk factors for severe varicella which require further study in adults and raises the need to discuss vaccination in at risk groups including appropriate counselling in those who may be at higher risk.
Publisher: MDPI AG
Date: 11-09-2023
Publisher: MDPI AG
Date: 03-2023
DOI: 10.20944/PREPRINTS202303.0021.V1
Abstract: This paper reports on a series of dynamic simple shear tests conducted to investigate the influence of particle shape on the d ing ratio of dry sand. The tests were conducted on sand s les subjected to simple cyclic shear tests to evaluate their cyclic behavior. The particle shape was quantified using three shape parameters: roundness, sphericity, and regularity. The sand s les were subjected to twelve different scenarios with varying vertical stresses and cyclic stress ratios (CSR), in both constant and controlled stress states. Each scenario involved five cyclic tests, using the same sand that was reconstructed from its previous cyclic test. After each cyclic test, hysteresis loops were created to determine the d ing ratio. The results showed that the shape of the sand particles changed during cyclic loading, becoming more rounded and spherical, which resulted in an increase in d ing ratio. Moreover, the paper presents two artificial intelligence models, an artificial neural network (ANN) and a support vector machine (SVM), which were developed to predict the effect of grain shape on the d ing ratio. The models were found to be effective in predicting the d ing ratio based on the shape of the grain, vertical stress, CSR, and number of loading cycles. Furthermore, a parameter analysis was conducted to identify the most important shape parameter, which was found to be vertical stress and regularity, while parameter CSR was the least important. Overall, this study contributes to a better understanding of the relationship between particle shape and d ing ratio, which could have practical implications for geotechnical engineering applications.
Publisher: Elsevier BV
Date: 06-2023
Publisher: MDPI AG
Date: 10-03-2023
DOI: 10.20944/PREPRINTS202303.0197.V1
Abstract: Alum sludge is a byproduct of water treatment plants and its use as a soil stabilizer has gained increasing attention due to its economic and environmental benefits. Its application has been shown to improve the strength and stability of soil, making it suitable for various engineering applications. However, to go beyond just measuring the effects of alum sludge as a soil stabilizer, this paper explores the use of artificial intelligence (AI) methods to predict the California bearing ratio (CBR) of soils stabilized with alum sludge. Three AI methods, including two black box methods (artificial neural network and support vector machines) and one grey box method (genetic programming), were used to predict CBR based on a database with nine input parameters. The results showed that all three AI models were able to predict CBR with good accuracy, with coefficient of determination (R2) values ranging from 0.94 to 0.99 and mean absolute error (MAE) values ranging from 0.30 to 0.51. In a novel approach, the genetic programming method was used to produce an equation to estimate CBR, which included seven inputs and accurately predicted CBR. The analysis of sensitivity and importance of parameters showed that the number of hammer blows for compaction was the most important parameter, while the parameters for maximum dry density of soil and mixture were the least important. This study suggests that AI methods can effectively predict the performance of alum sludge as a soil stabilizer, and the proposed equation using genetic programming can be a useful tool for predicting CBR.
Publisher: Wiley
Date: 05-05-2022
DOI: 10.1111/AJD.13866
No related grants have been discovered for Abolfazl Baghbani.