Search

Triangles and trigonometry are always difficult topics for both mathematics students and teachers. Hence, students' performance in solving mathematical word problems in these topics is not only a reflection of their learning outcomes but also an indication of teaching effectiveness. This case study drew from two examples of solving word problems involving triangles by pre-service mathematics teachers in a foundation mathematics course delivered by the author. The focus of this case study was on reasoning implications of students' performances on the effective training of pre-service mathematics teachers, from which a three-step interactive explicit teaching-learning approach, comprising teacher-led precise and inspiring teaching (or explicit teaching), student-driven engaged learning (or imitative learning), and student-led and teacher-guided problem-solving for real-world problems or projects (or active application), was summarized. Explicit teaching establishes a solid foundation for students to further their understanding of new mathematical concepts and to conceptualize the technical processes associated with these new concepts. Imitative learning helps students build technical abilities and enhance technical efficacy by engaging in learning activities. Once these first two steps have been completed, students should have a decent understanding of new mathematical concepts and technical efficacy to analyze, formulate, and finally solve real-world applications with assistance from teachers whenever required. Specially crafted professional development should also be considered for some in-service mathematics teachers to adopt this three-step interactive teaching-learning process.

BackgroundCellulitis is a bacterial skin infection that tends to recur. Previous studies have identified several risk factors that may contribute to its pathogenesis. Obesity is an increasingly prevalent worldwide disease that has been associated with skin and soft tissue infections. ObjectiveThe aim of our systematic review and meta-analysis was to investigate the association of cellulitis with obesity. MethodsThe Ovid MEDLINE, Embase, Cochrane Central Register of Controlled Trials, and Web of Science databases were searched for the relevant studies from the inception of each respective database to March 13, 2021. Case-control, cross-sectional, or cohort studies that examined the odds or risk of increased BMI in patients with cellulitis were included. This study was carried out in accordance with the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. The Newcastle-Ottawa scale (NOS) was used to evaluate the risk of bias in included studies. ResultsIn total, 9 case-control studies were included in our quantitative meta-analysis with a total of 68,148 study participants. A significant association was found between cellulitis and obesity (pooled odds ratio [OR] 2.67, 95% CI 1.91-3.71). No significant association was observed between cellulitis and being overweight (pooled OR 1.69, 95% CI 0.99-2.88). Patients with cellulitis were also found to have 1.63-fold increased odds of being male (pooled OR 1.63, 95% CI 1.12-2.38). ConclusionsOur findings suggest that cellulitis is significantly associated with obesity. Maintaining a healthy BMI may be indicated for patients presenting with cellulitis.

Studies have shown that solving worded real-world problems is a difficult challenge for pre-service secondary mathematics teachers engaging study in most tertiary institutions. This case study reports the performances of three groups of regional pre-service mathematics teachers in their attempts to solve three different worded real-world problems through simultaneous equations in their formal assessments. These students were the first-year pre-service mathematics teachers enrolled in an undergraduate education program in a regional university in Australia. Their performances are compared by statistical analysis. The result of this study indicates that design of the word questions should consider challenging tasks appropriate for the students to achieve the pedagogical purpose in solving real-life problems that can best facilitate training the students towards becoming knowledgeable and motivated mathematics teachers for secondary schools, rather than ideologically making a levelling field with less challenging problems to achieve a high pass rate mandated by many institutions.

The trapezium and Simpson's methods are widely used for numerical integration. In most circumstances, Simpson's method is more accurate than the trapezium method but only applicable to cases with equal intervals of even numbers. This technical note reports the formulation of two modified Simpson's methods, the trapezium-corrected Simpson's method (TCSM) and cubic-corrected Simpson's method (CCSM), as general-purpose symmetric formulas to solve numerical integrations with equal intervals of odd numbers (n ≥ 5) with the same level of accuracy as that of Simpson's method applied to the even number near n. Error analysis in terms of the order of error bound and case studies in this note demonstrate and validate the usefulness of the proposed formulas for solving different types of theoretical problems and real-world applications. In terms of accuracy of approximation for cases with equal intervals of odd numbers, CCSM performs better than TCSM by at least one order in error bound whereas TCSM performs better than the trapezium method by at least one order in error bound.

Numerical integration plays an important role in solving various engineering and scientific problems and is often learnt in applied calculus commonly through the trapezium and Simpson's methods (or rules). A common misconception for some students is that Simpson's method is the default choice for numerical integration due to its higher accuracy in approximation over the trapezium method by overlooking the requirement for using Simpson's method. As learning progressed to other numerical methods scheduled later in advanced mathematics, such as interpolations and computational modelling using computing tools like MATLAB, there is a lack of articulation among these numerical methods for students to solve problems solvable only by combining two or more approaches. This classroom note shares a few teaching and learning practices the author experienced in lectures, tutorials, and formal assessments on comparing or combining different numerical methods for numerical integration for engineering students in applied calculus and advanced mathematics over the past decade at Central Queensland University (CQU), a regional university in Australia. Each case represents a common concern raised or a mistake made by some students in different times. These efforts helped not only correct the misconception on the use of Simpson's method by some students, but also develop students' strategic thinking in problem solving, particularly involving decision-making for choosing the best possible method to produce a more appropriate solution to a problem that does not have an analytical solution.

The genesis models of the iron-ores hosted in banded iron formations (BIFs) in the Hamersley Province of Western Australia have been debated since the iron-ore deposits were discovered in the 1960s. The existing models considered the few physicochemical conditions for the iron-ore enrichment from BIFs. This study incorporates the latest research outcomes in conversions among the major magnetic minerals under different physicochemical conditions with the thermal magnetic analysis for BIFs and iron-ores collected from the Hamersley Province to fill the gap in knowledge highlighted by existing studies of the iron ores and BIFs. The results indicate that the high-grade hematite ores might have been undergone a physicochemical process under hydrothermal conditions between 120 ℃ and 220 ℃ during the major stage of enrichment from the original BIFs in the Brockman Iron Formation. Such physicochemical conditions would require either that the BIF units were buried 4000–5000 m underground with tilted broad channels formed by large-scale deformation in the region that facilitates hydrothermal reactions and leaching by the fluids flowing down deep to 4000–5000 m, somehow similar to the deep-seated supergene model proposed in previous works, or that the BIF units were still buried but the hydrothermal fluids coming up from deeper sources spread widely over the broad channels to ensure the high-grade hematite ores are consistently uniform over the entire deposit. The large-scale martite-goethite deposits in the Marra Mamba Iron Formation might be derived from multiple supergene phases from hematite-martite ores below 100 ℃ in the natural process of oxidization near surface, somewhat similar to the existing model for the channel iron deposits. Magnetite contained within current BIFs and iron ores was least likely derived from primary hematite in BIFs.

The unmanned aerial vehicle (UAV), as a remote sensing platform, has attracted many researchers in precision agriculture because of its operational flexibility and capability of producing high spatial and temporal resolution images of agricultural fields. This study proposed machine learning (ML) models and their ensembles for peanut yield prediction using UAV multispectral data. We utilized five bands (red, green, blue, near-infra-red (NIR) and red-edge) multispectral images acquired at various growth stages of peanuts using UAV. The correlation between spectral bands and yield was analyzed for each growth stage, which showed that the maturity stages had a significant correlation between peanut yield and spectral bands: red, green, NIR and red edge (REDE). Using these four bands spectral data, we assessed the potential for peanut yield prediction using multiple linear regression and seven non-linear ML models whose hyperparameters were optimized using simulated annealing (SA). The best three ML models, random forest (RF), support vector machine (SVM) and XGBoost, were then selected to construct a cooperative yield prediction framework with both the best ML model and the ensemble scheme from the best three as comparable recommendations to the farmers.

Two hand-on workshops on social media apps were conducted for the Year-12 students from two schools, one from a regional city and the other from a remote community, in a computer laboratory on the Rockhampton campus at Central Queensland University before the COVID-19 pandemic. The school in the regional city offered a specialist Digital Technologies Curriculum (DTC) to students in Years 11 & 12 whereas the remote school did not offer a similar DTC to students in Years 11 & 12. Statistical analyses of the students' responses to two casual questions during the workshop indicated that firstly the hands-on activities improved all students' general IT knowledge, and secondly the Year-12 students from the regional city were more determined to undertake tertiary IT education than the students from the remote school. Therefore, it is recommended that a mandatory specialist DTC for students in Years 11 & 12 in ALL schools should be included in the national curriculum in the future. Implications of these findings on improving the participation rate of post-secondary education in Australian regional communities are also discussed in this article. In particular, regional universities can play a unique role in producing "IT allrounders" to meet the needs of the regional communities through collaborations with governments, secondary schools, regional industries and businesses.

Background: While deep brain stimulation (DBS) therapy can be effective at suppressing tremor in individuals with medication-refractory Essential Tremor, patient outcome variability remains a significant challenge across centers. Proximity of active electrodes to the cerebellothalamic tract (CTT) is likely important in suppressing tremor, but how tremor control and side effects relate to targeting parcellations within the CTT and other pathways in and around the ventral intermediate (VIM) nucleus of thalamus remain unclear. Methods: Using ultra-high field (7T) MRI, we developed high-dimensional, subject-specific pathway activation models for 23 directional DBS leads. Modeled pathway activations were compared with post-hoc analysis of clinician-optimized DBS settings, paresthesia thresholds, and dysarthria thresholds. Mixed-effect models were utilized to determine how the six parcellated regions of the CTT and how six other pathways in and around the VIM contributed to tremor suppression and induction of side effects. Results: The lateral portion of the CTT had the highest activation at clinical settings (p

BackgroundRosacea is a chronic inflammatory skin condition that predominantly manifests as facial flushing, irritation, and acne. Rosacea and cancer are thought to be linked by the commonality of inflammatory and immune response dysfunction. Studies that have looked into this possible association have reported mixed results. ObjectiveGiven the conflicting literature on this topic, our study sought to evaluate the overall association between rosacea and several cancers commonly investigated in the literature. MethodsA systematic review was conducted using the Cochrane, PubMed, Embase, and Ovid databases. Studies were screened independently for inclusion of rosacea and glioma and breast, thyroid, hepatic, or skin cancers. Using information from the articles, rosacea and each cancer were categorized as having a positive, negative, or unclear association. ResultsOur systematic review included 39 full-text studies that investigated the association between rosacea and various malignancies. Among the malignancies of concern, 41% (16/39) of the studies reported an association with basal cell carcinoma, with 2 cohorts revealing an adjusted risk ratio (RR) of 1.50 (95% CI 1.35-1.67) and 0.72 (95% CI 0.56-0.93). In total, 33% (13/39) of the studies reported an association with squamous cell carcinoma, with 2 cohorts revealing an adjusted RR of 1.4 (95% CI 1.02-1.93) and 1.30 (95% CI 0.90-1.88). A total of 8% (3/39) of the studies reported an association between breast cancer and melanoma, with breast cancer cohorts revealing an adjusted RR of 8.453 (95% CI 1.638-43.606), 1.03 (95% CI 0.89-1.20), and 1.36 (95% CI 1.18-1.58) and melanoma cohorts revealing an adjusted RR of 1.10 (95% CI 0.95-1.27), 0.63 (95% CI 0.47-0.85), and 0.96 (95% CI 0.57-1.62). A total of 5% (2/39) of the studies reported an association among nonmelanoma skin cancers, hepatic cancer, and thyroid carcinomas, with nonmelanoma skin cancer cohorts revealing an adjusted RR of 1.36 (95% CI 1.26-1.47) and 2.66 (95% CI 1.53-4.61), hepatic cancer cohorts revealing an adjusted RR of 1.42 (95% CI 1.06-1.90) and 1.32 (95% CI 0.89-1.95), and thyroid carcinoma cohorts revealing an adjusted RR of 1.06 (95% CI 0.68-1.65) and 1.59 (95% CI 1.07-2.36). Only 1 cohort reported an association with glioma, revealing an adjusted RR of 1.36 (95% CI 1.18-1.58). According to our review, patients with rosacea were statistically more likely to have nonmelanoma skin cancers, breast cancer, and glioma. Rosacea was not found to be substantially associated with melanoma. The associations between rosacea and hepatic and thyroid cancers were unclear because of conflicting results. ConclusionsThe current literature shows that rosacea is significantly associated with increased odds of nonmelanoma skin cancers, glioma, and breast cancer. Rosacea does not appear to be associated with melanoma. Further studies should be conducted to clarify the association between thyroid and hepatic cancers and rosacea.

The Hamersley Province in the northwest of Western Australia contains extensive banded iron formations (BIFs) and large hematite-goethite deposits. Density information of rocks and ores in this region has been scarce. This study reports the results of a systematic density investigations based on more than eight hundred density datasets in the province. This study not only provides a better understanding of density distribution of the rocks and ores in the province, but also allows forward gravity modeling over the known iron-ore deposits to be conducted for exploring the usefulness and effectiveness of gravity surveys for detecting concealed iron-ore deposits in the region. This should have a significant impact on iron-ore mining in the province as the outcropped ores have been mined for over 40 years in the province and the future targets are likely the concealed deposits below the surface. The analysis shows a clear density contrast around 1.0 g/cm3 between the Brockman iron ores and the host BIFs, which should generate clear positive net gravity anomalies over buried large iron-ore deposits. However, porous goethite ores hosted in the Marra Mamba BIFs have an average density of about 2.8 g/cm3 due to porosity about 30–40% in the ores. A density contrast of −0.5 g/cm3 may exist between the goethite ores and BIFs, which would produce net negative gravity anomalies over the deposits. Since most goethite deposits are layered consistently with the host rocks and associated with broad folds, the net gravity anomaly of an orebody itself may generally have the similar shape to the corresponding BIF bedrock. This implies that gravity surveys may be able to detect paleochannels which host the goethite ores, rather than directly detecting the orebody.

Newton's method is a popular numeric approach due to its simplicity and quadratic convergence to solve nonlinear equations that cannot be solved with exact solutions. However, the initial point chosen to activate the iteration of Newton's method may cause difficulties in slower convergence, stagnation, and divergence of the iterative process. The common advice to deal with these special cases was to choose another inner point to repeat the process again or use a graph of the nonlinear equation (function) to choose a new initial point. Based on the recent experiences in teaching preservice secondary-school mathematics teachers, this classroom note presents a simple and practical strategy to avoid many of the difficulties encountered in using Newton's method during solving nonlinear equations. Instead of plotting the graph of an equation to help choose the initial point, the practical strategy is to use the middle point of the range as the initial point to start the iterative process of Newton's method. By solving ten different nonlinear equations using Newton's method initiated from the endpoints and the middle point respectively, the results show that choosing the middle point of a defined range to initiate the iterative process for Newton's method seems a general and practical strategy to avoid the difficulties encountered in using the method to solve nonlinear equations with faster convergence in most cases.

For students who are academically ineligible to enter a bachelor program in engineering but still want to upskill their knowledge in engineering, many universities provide an associate degree program in engineering to these students. The higher achievers from the associate degree program can transfer to a full degree program in engineering. Mathematics courses in such associate degree programs are often challenging to both the teachers and students due to various reasons. This paper presents a small part of a mathematics revitalization project on pedagogical adjustment to scaffold mathematics learning for students in an associate engineering program at Central Queensland University (CQU), a regional university in Australia, from 2018 to 2020. The design and implementation of the online multi-purpose quizzes (MPQ) to improve both the learning environment and outcomes for the engineering students from 2018 to 2020 are reported in this work. Statistically, the online MPQ empowered students to achieve their best possible outcomes by attempting the questions with time flexibility, on a confined set of topics, and with more chances of amending errors than the traditional written assessments. Hence, their performance in the online MPQ was consistently better than that in the written assignments in 2018-2020. The weaknesses of the online MPQ are also discussed.

Because of the recent development in advanced sensors, data acquisition platforms, and data analysis methods, unmanned aerial vehicle (UAV) or drone-based remote sensing has gained significant attention from precision agriculture (PA) researchers. The massive amount of raw data collected from such sensing platforms demands large-scale data processing algorithms such as machine learning and deep learning methods. Therefore, it is timely to provide a detailed survey that assimilates, categorises, and compares the performance of various machine learning and deep learning methods for PA. This paper summarises and synthesises the recent works using a general pipeline of UAV-based remote sensing for precision agriculture research. We classify the different features extracted from UAV imagery for various agriculture applications, showing the importance of each feature for the performance of the crop model and demonstrating how the multiple feature fusion can improve the models' performance. In addition, we compare and contrast the performances of various machine learning and deep learning models for three important crop trait estimations: yield estimation, disease detection and crop classification. Furthermore, the recent trends in applications of UAVs for PA are briefly discussed in terms of their importance, and opportunities. Finally, we recite the potential challenges and suggest future avenues of research in this field.

Based on the tutorial cases accumulated in the past several years, by reclassifying the arithmetic functions (A) in LIATE into the polynomial function (P), the standard power function (Po), and the integer power function (nPo), a new guide, comprising three sub-guides, Pet, LoPo and InPo, or Pet-LoPo-InPo, is summarized in this note to guide practicing integration by parts. This new guide removes many incompatible combinations included in LIATE, rationalizes the relationship between the exponential and trigonometric functions in LIATE, and expands the coverage of the P-functions beyond the traditional definitions. Hence, the new guide can reduce potential confusions that students may experience in using LIATE for their practices of integration by parts. The advantages of this new guide are demonstrated by many worked examples in this note.

Semantic segmentation has been widely used in precision agriculture, such as weed detection, which is pivotal to increasing crop yields. Various well-established and swiftly evolved AI models have been developed of late for semantic segmentation in weed detection; nevertheless, there is insufficient information about their comparative study for optimal model selection in terms of performance in this field. Identifying such a model helps the agricultural community make the best use of technology. As such, we perform a comparative study of cutting-edge AI deep learning-based segmentation models for weed detection using an RGB image dataset acquired with UAV, called CoFly-WeedDB. For this, we leverage AI segmentation models, ranging from SegNet to DeepLabV3+, combined with five backbone convolutional neural networks (VGG16, ResNet50, DenseNet121, EfficientNetB0 and MobileNetV2). The results show that UNet with EfficientNetB0 as a backbone CNN is the best-performing model compared with the other candidate models used in this study on the CoFly-WeedDB dataset, imparting Precision (88.20%), Recall (88.97%), F1-score (88.24%) and mean Intersection of Union (56.21%). From this study, we suppose that the UNet model combined with EfficientNetB0 could potentially be used by the concerned stakeholders (e.g., farmers, the agricultural industry) to detect weeds more accurately in the field, thereby removing them at the earliest point and increasing crop yields.

Integration by parts can be applied in various ways for obtaining solutions for different types of integrations and hence it is taught in all calculus courses in the world. However, the coverage and discourse of various applications of integration by parts in most textbooks, often packed into one section, lack a cohesion of progression for solving different types of integrals. Students may be confused by such incohesive presentation of the method and applications in the textbooks. Based on the author's experiences and practices in teaching applied calculus for undergraduate engineering and education students since 2013, a streamlined approach in teaching integration by parts has been gradually developed to the current state and ready to be shared with the mathematics teaching and learning communities. This streamlined approach allows integration by parts to be applied to solve complicated and integrated problems in a progressive way so that students can improve efficacy in their use of integration by parts gradually. This approach also makes communications easier with students on particular problems involving integration by parts.

In Australia, peanuts are mainly grown in Queensland with tropical and subtropical climates. The most common foliar disease that poses a severe threat to quality peanut production is late leaf spot (LLS). Unmanned aerial vehicles (UAVs) have been widely investigated for various plant trait estimations. The existing works on UAV-based remote sensing have achieved promising results for crop disease estimation using a mean or a threshold value to represent the plot-level image data, but these methods might be insufficient to capture the distribution of pixels within a plot. This study proposes two new methods, namely measurement index (MI) and coefficient of variation (CV), for LLS disease estimation on peanuts. We first investigated the relationship between the UAV-based multispectral vegetation indices (VIs) and the LLS disease scores at the late growth stages of peanuts. We then compared the performances of the proposed MI and CV-based methods with the threshold and mean-based methods for LLS disease estimation. The results showed that the MI-based method achieved the highest coefficient of determination and the lowest error for five of the six chosen VIs whereas the CV-based method performed the best for simple ratio (SR) index among the four methods. By considering the strengths and weaknesses of each method, we finally proposed a cooperative scheme based on the MI, the CV and the mean-based methods for automatic disease estimation, demonstrated by applying this scheme to the LLS estimation in peanuts.

The Laplace transform is a popular approach in solving ordinary differential equations (ODEs), particularly solving initial value problems (IVPs) of ODEs. Such stereotype may confuse students when they face a task of solving ODEs without explicit initial condition(s). In this paper, four case studies of solving ODEs by the Laplace transform are used to demonstrate that, firstly, how much influence of the stereotype of the Laplace transform was on student's perception of utilizing this method to solve ODEs under different initial conditions; secondly, how the generalization of the Laplace transform for solving linear ODEs with generic initial conditions can not only break down the stereotype but also broaden the applicability of the Laplace transform for solving constant-coefficient linear ODEs. These case studies also show that the Laplace transform is even more robust for obtaining the specific solutions directly from the general solution once the initial values are assigned later. This implies that the generic initial conditions in the general solution obtained by the Laplace transform could be used as a point of control for some dynamic systems.

Special tutorials both online and off-line were experimented in order to provide extra support for the senior pre-service mathematics teachers at an Australian regional university to improve their learning experience and achieve the best possible learning outcomes in an advanced mathematics course focusing on solving ordinary differential equations and applying mathematical modelling. Two types of special tutorials were offered to the students, the progressive tutorials on solving the same problem with different methods according to the learning progression and student's instant requests, and the targeted tutorials to address the common problems shared by many students in attempting questions in the formal assessments. The experiments on these special tutorials indicated that the targeted tutorials were immensely useful for the students to either expand the scientific knowledge related to a real-world scenario described by words so as to begin problem solving with correct setting-ups or streamline multiple mathematical processes together to solve a complicated real-world problem described in words. This approach motivated most students to achieve their best possible learning outcomes. The progressive tutorials were effective in addressing student's curiosity of solving the same problem by multiple techniques and hence improving student's mathematical thinking and problem-solving skills in general. This exploratory study also found that there were common problems with a lack of general science knowledge and retention of the previously learnt mathematical techniques among most students. There also existed a portion of students who showed no interest in engaging with learning regardless of how much extra learning support provided to them.

A good understanding of the mathematical processes of solving the first-order linear ordinary differential equations (ODEs) is the foundation for undergraduate students in science and engineering programs to progress smoothly to advanced ODEs and/or partial differential equations (PDEs) later. However, different methods for solving the first-order linear ODEs are presented in various textbooks and resources, which often confuses students in their choice of the method for solving the ODEs. This special tutorial note presents the practices the author used to address such confusions in solving the first-order linear ODEs for students engaged in the bachelorette engineering studies at a regional university in Australia in recent years. The derivation processes of the four commonly adopted methods for solving the first-order linear ODEs, including three explicit methods and one implicit method presented in many textbooks, are presented first, followed by the logical interconnections that unify these four methods to clarify student's confusions on different presentations of the procedures and the solutions in different sources. Comparisons among these methods are also made.

Because of the recent advances in drones or Unmanned Aerial Vehicle (UAV) platforms, sensors and software, UAVs have gained popularity among precision agriculture researchers and stakeholders for estimating traits such as crop yield and diseases. Early detection of crop disease is essential to prevent possible losses on crop yield and ultimately increasing the benefits. However, accurate estimation of crop disease requires modern data analysis techniques such as machine learning and deep learning. This work aims to review the actual progress in crop disease detection, with an emphasis on machine learning and deep learning techniques using UAV-based remote sensing. First, we present the importance of different sensors and image-processing techniques for improving crop disease estimation with UAV imagery. Second, we propose a taxonomy to accumulate and categorize the existing works on crop disease detection with UAV imagery. Third, we analyze and summarize the performance of various machine learning and deep learning methods for crop disease detection. Finally, we underscore the challenges, opportunities and research directions of UAV-based remote sensing for crop disease detection.

Commercialization and internationalization of tertiary education has opened a new way for universities to grow and make more profit. This in turn has supported sustainable growth of the higher education sector for the last three decades in developed countries. Curricula offered by accredited tertiary institutions must meet the quality standards set by both the governmental agencies and the professional accreditation bodies. These programs must also provide graduates with employment opportunities. Hence, quality and employment opportunities are the two key factors for sustainability of any degree program offered by tertiary institutions. However, changes in regulations and policies by the national government sometimes play a vital role in creating new programs, and maintaining or disestablishing some existing programs offered by institutions in the nation. These changes are not controlled by individual institutions, which has become the third unpredictable factor in curriculum creation and/or sustainability. Using the journey of a new master's program in information technology (IT) in an Australian university as a case study, we explore how this third factor impacted on the initialization, creation, and short life of this program primarily targeting international students in the mid-2000s. We then extend our discussion to the implications of the recent changes to tertiary tuitions from 2021 by the Australian Government on the sustainable future of the Australian tertiary education sector on a broad scale.

BackgroundMetabolic syndrome (MetS) has been associated with various skin conditions including vitiligo. However, the association between these 2 conditions has yet to be determined by quantitative meta-analysis. ObjectiveThe aim of this paper was to determine the association between vitiligo and metabolic syndrome via systematic review and meta-analysis. MethodsA systematic literature search of Pubmed, Embase, Cochrane, and Web of Science was performed for all published literature prior to August 16, 2020. Case control and prospective cross-sectional studies analyzing the association between vitiligo and MetS were included in this review. The primary outcome measures include the type of vitiligo, diagnostic criteria for MetS, components of MetS (waist circumference, blood pressure, triglycerides, fasting glycemic index, and high-density lipoprotein cholesterol), low-density lipoprotein cholesterol levels, and BMI. A meta-analysis was performed to evaluate the prevalence and association of MetS in patients with vitiligo. ResultsA total of 6 studies (n=734 participants) meeting eligibility criteria were included for systematic review and meta-analysis. The pooled prevalence of MetS in patients with vitiligo was (0.296, 95% CI 0.206, 0.386; P

Recent deep learning methods for fruits classification resulted in promising performance. However, these methods are with heavy-weight architectures in nature, and hence require a higher storage and expensive training operations due to feeding a large number of training parameters. There is a necessity to explore lightweight deep learning models without compromising the classification accuracy. In this paper, we propose a lightweight deep learning model using the pre-trained MobileNetV2 model and attention module. First, the convolution features are extracted to capture the high-level object-based information. Second, an attention module is used to capture the interesting semantic information. The convolution and attention modules are then combined together to fuse both the high-level object-based information and the interesting semantic information, which is followed by the fully connected layers and the softmax layer. Evaluation of our proposed method, which leverages transfer learning approach, on three public fruit-related benchmark datasets shows that our proposed method outperforms the four latest deep learning methods with a smaller number of trainable parameters and a superior classification accuracy. Our model has a great potential to be adopted by industries closely related to the fruit growing and retailing or processing chain for automatic fruit identification and classifications in the future.

Studies have shown that solving real-world problems involving triangles is one of the most difficult topics for the pre-service secondary mathematics teachers engaging study and training in metropolitan institutions. We have known little about performances of the pre-service mathematics teachers from regional, rural and remote (RRR) areas engaging study and training in regional universities. This case study intends to explore whether solving word problems involving triangles would also be challenging for the RRR pre-service mathematics teachers, and what unique factors may negatively affect the RRR student teachers’ performances in solving word problems involving triangles. This study compared the works of two groups of the first-year pre-service mathematics teachers who enrolled in an undergraduate education program in a regional university in Australia. The two chosen word questions for comparison were parts of the assignments involving solving triangles to the students. Through statistical analysis, this study indicates that the considerable proportion of no attempts among the enrolled RRR students is the distinctive difference between the metropolitan and RRR pre-service mathematics teachers. Although still challenging, the RRR student teachers who attempted the word problems seemed performed better than the metropolitan students in solving word problems involving triangles.

Computational-intelligence methods in bioinformatics and systems biology show promising potential for leveraging abundant, large-scale molecular data. These methods can facilitate analysis and prediction of the principles of biological systems through the construction of statistical and visualized models. Specifically, structural data from exogenous and endogenous protein-protein interactions are of vital significance in this context, encompassing primarily 3-D structural information for a cohort of macromolecules underpinning the biological system. In this paper, we surveyed the main methodologies and algorithms for the reconstruction and modeling of the structural-interaction networks (SINs) of host- pathogen protein-protein interactions (HPPPIs), regarding how the protein domains interact with each other to constitute a SIN. Surveying the pattern and the organization of the SIN delivers a state-of-theart view of HPPPIs and illustrates prospective future research directions. In addition to the binary PPI network, we distilled the relevant data sources into several branching research areas and further expanded the discussions into computational-intelligence methods according to the algorithms applied, including machine learning statistical models, to shed light on effective method design. In particular, atomic resolution level investigations can reveal novel insights into the underlying principles of the organization and the complexity of HPPPIs networks. Combining data analytics and machine-learning technologies, we anticipate that our systematic overview will serve as a useful guide for interested researchers to carry out related studies on this exciting and challenging research topic in system biology.

An alternative pedagogical design is discussed that aims to guide engineering students to solve first-order ordinary differential equations (ODEs), and is based on students’ learning weaknesses identified from previous teaching and learning activities. This approach supported student’s self-enrichment through exploration of relevant resources in ODEs, and guided students towards the choice of their own effective ways for solving ODEs for different problems. This paper presents the practices on designing and delivering solution techniques for first-order linear ODEs using this approach for more than 400 undergraduate engineering students at a regional university in Australia during 2014–2017. The timeline involved initial experimentation in 2014 and 2015, followed by refinements to the pedagogy based on student’s feedback. The refined pedagogy was then used for the advanced mathematics course in 2016 and 2017. Significant improvements were made in student’s learning outcomes in effectively and accurately solving the first-order linear ODEs over this period.

The production–inventory system is a problem of multivariable input and multivariant output in mathematics. Selecting the best system control parameters is a crucial managerial decision to achieve and dynamically maintain an optimal performance in terms of balancing the order rate and stock level under dynamic influence of many factors affecting the system operations. The dynamic performance of the popular APIOBPCS model and the newly modified 2APIOBPCS model for optimal control of production–inventory systems is examined in the study. This examination is based on the leveled ground with a new simulation scheme that incorporates a designated multi-objective particle swarm optimization (MOPSO) algorithm into the simulation, which enables the optimal set of system control parameters to be selected for achieving the situational best possible performance of the production–inventory system under study. The dynamic performance is measured by the variance ratio between the order rate and the sales rate related to the bullwhip effect, and the integral of absolute error related to the inventory responsiveness in response to a random customer demand. Our simulation indicates that the 2APIOBPCS model performed better than or at least no worse than, and more robust than the APIOBPCS model under different conditions.

The long short-term memory (LSTM) and gated recurrent unit (GRU) models are popular deep-learning architectures for stock market forecasting. Various studies have speculated that incorporating financial news sentiment in forecasting could produce a better performance than using stock features alone. This study carried a normalized comparison on the performances of LSTM and GRU for stock market forecasting under the same conditions and objectively assessed the significance of incorporating the financial news sentiments in stock market forecasting. This comparative study is conducted on the cooperative deep-learning architecture proposed by us. Our experiments show that: (1) both LSTM and GRU are circumstantial in stock forecasting if only the stock market features are used; (2) the performance of LSTM and GRU for stock price forecasting can be significantly improved by incorporating the financial news sentiments with the stock features as the input; (3) both the LSTM-News and GRU-News models are able to produce better forecasting in stock price equally; (4) the cooperative deep-learning architecture proposed in this study could be modified as an expert system incorporating both the LSTM-News and GRU-News models to recommend the best possible forecasting whichever model can produce dynamically.

Analytical CT reconstruction is popular in practice because of its computational efficiency, but it suffers from low reconstruction quality when an insufficient number of projections are used. To address this issue, this paper presents a new analytical method of backprojection Wiener deconvolution (BPWD). BPWD executes backprojection first, and then applies a Wiener deconvolution to the whole backprojected image. The Wiener filter is derived from a ramp filter, enabling the proposed approach to perform reconstruction and denoising simultaneously. The use of a filter after backprojection does not differentiate between real sampled projections and interpolated ones, introducing reconstruction errors. Therefore a weighted ramp filter was applied to increase the contribution of real sampled projections in the reconstruction, thus improving reconstruction quality. Experiments on synthetic data and real phase-contrast x-ray images showed that the proposed approach yields better reconstruction quality compared to the classical filtered backprojection (FBP) method, with comparable reconstruction speed.