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Blockchain for Smart Logistics:Enhancing Identity Security, Bidding Transparency and Goods Tracking
Get PDFLogistics Management Systems (LMS) are crucial to global supply chains, ensuring efficient planning, data analysis and control of various logistics management processes. However, traditional LMS face complex challenges in mitigating cyber threats and data breaches. Blockchain technology emerges as a revolutionary solution, equipping LMS with traceability, immutability, and anonymity. While previous solutions focus on blockchain's theoretical potential or its application to singular aspects of logistics such as goods tracking or auction facilitation, our proposed Decentralized Logistics Management System (DLMS) distinguishes itself in two ways: first, driven by practical insights, we investigate the concurrent challenges and the advantages of blockchain that can be integrated into industrial LMS; second, we design a blockchain-based Decentralized Logistic Management System (DLMS) that includes comprehensive functional modules, including identity authentication, fair bidding mechanisms, and goods tracking. Finally, we implement a prototype of our proposed system and deploy the prototype on the Ethereum testnet to evaluate the performance of the proposed architecture. The experimental findings demonstrate the efficiency of our system and its potential as an effective solution to the identified challenges.</p
DNA methylation biomarker analysis from low-survival-rate cancers based on genetic functional approaches
Get PDFIdentifying cancer biomarkers through DNA methylation analysis is an efficient approach toward the detection of aberrant changes in epigenetic regulation associated with early-stage cancer types. Among all cancer types, cancers with relatively low five-year survival rates and high incidence rates were pancreatic (10%), esophageal (20%), liver (20%), lung (21%), and brain (27%) cancers. This study integrated genome-wide DNA methylation profiles and comorbidity patterns to identify the common biomarkers with multi-functional analytics across the aforementioned five cancer types. In addition, gene ontology was used to categorize the biomarkers into several functional groups and establish the relationships between gene functions and cancers. ALX3, HOXD8, IRX1, HOXA9, HRH1, PTPRN2, TRIM58, and NPTX2 were identified as important methylation biomarkers for the five cancers characterized by low five-year survival rates. To extend the applicability of these biomarkers, their annotated genetic functions were explored through GO and KEGG pathway analyses. The combination of ALX3, NPTX2, and TRIM58 was selected from distinct functional groups. An accuracy prediction of 93.3% could be achieved by validating the ten most common cancers, including the initial five low-survival-rate cancer types.</p
A dual-image fusion instance segmentation model for pavement patch detection
No full textAccurate patch detection is essential for reliable pavement condition evaluation and life cycle assessment. However, this task remains challenging due to variations in patch morphology, visual similarity to the background, and the limited availability of comprehensive patch datasets. This paper presents a novel patch detection method that pioneers the use of instance segmentation techniques to obtain more detailed patch information and fuses dual-image data from the Laser Crack Measurement System (LCMS) to capture richer features for enhanced precision. Furthermore, the proposed method goes beyond conventional approaches that focus solely on basic detection by incorporating a patch counting method, enabling accurate patch quantification and area measurement across different road section lengths. Experimental results show that the proposed patch detection model (FuPatch) outperforms baseline models while maintaining comparable efficiency. Additionally, the patch counting method effectively quantifies both the number and area of patches. These findings demonstrate that the developed model not only effectively detects patches but also provides detailed spatial insights and performs accurate patch counting, making it highly applicable for real-world pavement condition assessments.</p
Requirements engineering for older adult digital health software:A systematic literature review
No full textContext: Growth of the older adult population has led to an increasing interest in technology-supported aged care. However the area has some challenges such as lack of care givers and limitations in understanding the emotional, social, physical, and mental well-being needs of older adults. Furthermore, there is a gap in the understanding between younger developers and ageing people of their requirements from digital systems. Digital health can play an important role supporting older adults’ well-being, emotional requirements, and social needs. Objective: We carried out a systematic review of the literature on RE for older adult digital health software. This was necessary to show the representatives of the current stage of understanding the needs of older adults in aged care digital health. Methods: Using established guidelines we developed a protocol, followed by the systematic search of eight databases. This resulted in 69 primary studies of high relevance, which were subsequently subjected to data extraction, synthesis, and reporting. Results: This systematic literature review highlights key RE processes used in digital health software for older people. It explored the key features developed for many digital solutions, utilization of technology for older user well-being and care, and the evaluations of proposed solutions. The review also identified key limitations found in existing primary studies that inspire future research opportunities. Conclusion: Our results indicate that requirements gathering and understanding have a significant variation between different studies. The differences are in the quality, depth, and techniques adopted for requirement gathering and this reason for these differences is largely due to uneven adoption of RE methods.</p
Children's mathematics concept learning of informal length measurement:Conceptual PlayWorld as an innovative approach in the beginning of primary school period
Get PDFMeasurement is an essential and valuable mathematics concept closely linked to everyday life and is often one of the first few mathematics concepts children learn in educational contexts. Currently, limited research exists that investigates how implementing imaginary play could create conditions in supporting children's learning of informal length measurement as they transition to school. To support children's learning of informal length measurement, this study adapted Li and Disney's (2021) Conceptual PlayWorld [CPW] in mathematics to conduct an educational experiment investigating how the implementation of CPW creates the conditions to support children's learning during the transition to school. We argue that in the CPW, the use of imagination and the teacher's dramatisation of the mathematics conceptual problems allowed opportunities for children to demonstrate and explore informal length measurement using their everyday understanding of concepts. In turn, it supports the teacher in embedding mathematical learning opportunities in the imaginary play context. CPW can be considered an alternative pedagogical approach that incorporates mathematical exploration through imaginary play, creates opportunities to support children to engage with and understand measurement concepts.</p
Never being aggressive is important:a nationally representative survey of aggressive driving in Australia
No full textThe Driving Anger Expression Inventory (DAX; Deffenbacher et al., 2002) is an established tool to measure the frequency of different types of aggressive driving (verbal, personal physical and aggressive use of the vehicle) as well as constructive responses to anger. This study assessed the applicability of DAX on a representative sample of 2,108 drivers from Australia (men = 49.8 %, women = 49.7 %, non-binary/gender-diverse = 0.05 %), ranging in age from 18 to 95 years (M = 46.5; SD = 17.8). Drivers completed an online survey comprising demographic information and the 15-item DAX. Importantly, the scale used in this study sought frequency responses across 5 categories (never, almost never, occasionally, often or almost always); whereas previous applications have not used a never response. Confirmatory Factor Analysis (CFA) showed that the four-factor structure was suitable for drivers in Australia. Multigroup CFA confirmed DAX was invariant across age, gender and main purpose of driving (work or leisure). Aggression was higher for men compared to women; work drivers compared to leisure drivers; and, drivers aged 26 to 39 years compared to younger (aged 18 to 25 years) or older drivers (aged 40 to 64 and 65 + years). After controlling for age and annual kilometres driven, higher scores on DAX were associated with increased odds of having been in a crash or having received a traffic fine in the past 12 months. The findings demonstrate that DAX is a viable tool to measure aggressive driving in Australia which should be administered with a never option.</p
Differentiated instruction in higher education:the experience and perceptions of five academics
Get PDFThe increasing diversity of students in higher education presents numerous advantages for the sector, yet it also imposes substantial challenges for educators. This study explored the experiences of five academics across two countries as they strive to implement differentiated instruction (DI) to accommodate the diverse needs of students. The primary objective is to identify five aspects of DI implementation, namely teaching strategies, challenges, strategies to overcome challenges, methods to evaluate, and educators’ perceptions. The study employs a qualitative research design, utilizing semi-structured interviews for data collection. The results of the study revealed that educators agreed that implementing DI requires quite a lot of resources from educators, such as time for preparation and planning, effort, and commitment. On the other hand, they also struggle with other obligations as administrators in their workplace. Experienced and junior educators employed distinct methods to address challenges, with the former utilizing forward planning and the latter concentrating on refining their skills in DI. Despite these variations, there is a common shared understanding among all educators that although implementing DI poses challenges, it remains both manageable and beneficial within the diverse higher education environment.</p
Cross-validatory model selection for Bayesian autoregressions with exogenous regressors
Get PDFBayesian cross-validation (CV) is a popular method for predictive model assessment that is simple to implement and broadly applicable. A wide range of CV schemes is available for time series applications, including generic leave-one-out (LOO) and K-fold methods, as well as specialized approaches intended to deal with serial dependence such as leave-future-out (LFO), h-block, and hv-block. Existing large-sample results show that both specialized and generic methods are applicable to models of serially-dependent data. However, large sample consistency results overlook the impact of sampling variability on accuracy in finite samples. Moreover, the accuracy of a CV scheme depends on many aspects of the procedure. We show that poor design choices can lead to elevated rates of adverse selection. In this paper, we consider the problem of identifying the regression component of an important class of models of data with serial dependence, autoregressions of order p with q exogenous regressors (ARX(p, q)), under the logarithmic scoring rule. We show that when serial dependence is present, scores computed using the joint (multivariate) density have lower variance and better model selection accuracy than the popular pointwise estimator. In addition, we present a detailed case study of the special case of ARX models with fixed autoregressive structure and variance. For this class, we derive the finite-sample distribution of the CV estimators and the model selection statistic. We conclude with recommendations for practitioners.</p
Exercising teacher agency in response to demands of practice:insights from an Australian TESOL online placement
Get PDFA Hypothesis-Driven Approach to Explainable Goal Recognition
No full textIn this paper, we introduce an explainable goal-recognition (XGR) approach for decision support that instantiates the evaluative AI paradigm. Current explainable AI (XAI) approaches focus on providing recommendations and justifying those recommendations. However, a shift toward evaluative AI has been proposed, focusing on generating evidence to support or refute human judgments and explaining trade-offs among hypotheses, rather than merely justifying AI recommendations. We introduce such a method for goal recognition tasks by leveraging the Weight of Evidence (WoE) framework. Through a human study in a maritime surveillance task, we demonstrate that our model improves decision accuracy, efficiency, and reliance in complex scenarios, outperforming two baseline models and demonstrating its potential in real-world decision-making.</p