Search

Paper based assays are paving the way to automated, simplified, robust and cost-effective point of care testing (POCT). We propose a method for fabricating three dimensional (3D) microfluidic paper based analytical devices (μPADs) via combining thin adhesive films and paper folding, which avoids the use of cellulose powders and the complex folding sequence and simultaneously permits assays in several layers. To demonstrate the effectiveness of this approach, a 3DμPADs was designed to conduct more assays on a small footprint, allowing dual colorimetric and electrochemical detections. More importantly, we further developed a 3D platform for implementing automated and multiplexed ELISA in parallel, since ELISA, a routine and standard laboratory method, has rarely been used in practical analyses outside of the laboratory. In this configuration, complex and multistep diagnostic assays can be carried out with the addition of the sample and buffer in a simple fashion. Using Troponin I as model, the device showed a broad dynamic range of detection with a detection limit of 0.35 ng/mL. Thus, the developed platforms allow for various assays to be cost-effectively carried out on a single 3D device, showing great potential in an academic setting and point of care testing under resource-poor conditions., (Copyright © 2019 Elsevier B.V. All rights reserved.)

Infectious diseases remain one of the leading causes of deaths in developing countries because of a lack of basic sanitation, healthcare clinics, and centralized laboratories. Paper-based rapid diagnostic tests, such as the lateral-flow immunoassay (LFA), provide a promising alternative to the traditional laboratory-based tests; however, they typically suffer from having a poor sensitivity. Biomarker preconcentration and signal enhancement are two common methods to improve the sensitivity of paper-based assays. While effective, these methods often require multiple liquid handling steps which are not ideal for use by untrained personnel in a point-of-care setting. Our lab previously discovered the phenomenon of an aqueous two-phase system (ATPS) separating on paper, which allowed for the seamless integration of concentration and detection of biomarkers on the LFA. In this work, we have extended the functionality of an ATPS separating on paper to automate the sequential delivery of signal enhancement reagents in addition to concentrating biomarkers. The timing of reagent delivery was controlled by changing the initial composition of the ATPS. We applied this technology to automate biomarker concentration and nanozyme signal enhancement on the LFA, resulting in a 30-fold improvement in detection limit over the conventional LFA when detecting Escherichia coli , all while maintaining a single application step.

Artificial intelligence tools have presented many challenges and opportunities to transform teaching and learning on college campuses. These changes are significant enough to require colleges to take action to create a framework by which faculty and students can navigate the proper usage of these tools. Rather than working to create entirely new policies strictly for addressing these new technologies, they should instead edit their existing academic integrity and intellectual property statements to incorporate explicit mentions of artificial intelligence.

This paper focuses on the opportunities and challenges associated with the use of artificial intelligence (AI) in academic library operations. In the quest to render fast, effective and efficient services, academic libraries have adopted different technologies in the past. Artificial intelligence technologies is the latest among the technologies currently being introduced in libraries. The technology which is considered an intelligent system, come in the form of robots and expert systems which have natural language processing, machine learning and pattern recognition capabilities. This paper examined the features of AI, the application of AI to library operations, examples of academic libraries with AI technologies in Sub-Saharan Africa, the need for AI in libraries and the challenges associated with the adoption of AI in libraries. The study concluded that AI holds a lot of prospects for the improvement of information services delivery in African academic libraries. Consequently, its adoption is a sinequanon to delivering robust library services in the Fourth Industrial Revolution (4IR).

Abstract Large language models (LLMs) are an exciting breakthrough in the rapidly growing field of artificial intelligence (AI), offering unparalleled potential in a variety of application domains such as finance, business, healthcare, cybersecurity, and so on. However, concerns regarding their trustworthiness and ethical implications have become increasingly prominent as these models are considered black-box and continue to progress. This position paper explores the potentiality of LLM from diverse perspectives as well as the associated risk factors with awareness. Towards this, we highlight not only the technical challenges but also the ethical implications and societal impacts associated with LLM deployment emphasizing fairness, transparency, explainability, trust and accountability. We conclude this paper by summarizing potential research scopes with direction. Overall, the purpose of this position paper is to contribute to the ongoing discussion of LLM potentiality and awareness from the perspective of trustworthiness and responsibility in AI.

Article highlights Automated control system for paper production has been developed, which has increased the accuracy and stability of regulating the weight of paper web. Fiber consumption rates decreased by 2%. The standard deviation of the weight of the paper web decreased by 1.5-2%.

The present study examined the psychometric equivalence of administering a computer-based version of the Gifted Rating Scale (GRS) compared with the traditional paper-and-pencil GRS-School Form (GRS-S). The GRS-S is a teacher-completed rating scale used in gifted assessment. The GRS-Electronic Form provides an alternative method of administering and completing the 72-item scale, which eases scoring and interpretation of the measure. The GRS-E reduces the potential for human error by implementing automated scoring. An automated form may increase the likelihood that the measure is utilized, supporting wider screening in the schools for gifted identification. Five of the six subscales were completed by teachers in matched pairs on 185 students. Results indicate strong internal consistency across both administration forms and no significant difference in mean scores. This study contributes to the growing body of literature in support of the psychometric equivalence of paper-and-pencil report forms administered in a computer-based environment.

Recovered paper is the most important raw material for paper industry today and paper industry is one of the forerunners of circular economy. However, despite the long history with recycling the characterization of recovered paper quality is still not solved sufficiently. Here the automatic classification of paper objects can bring significant progress. Up to now, most technical approaches use NIR sensors and visual cameras, which can only “see” the surface of the objects. This often leads to misclassifications, since, for example, many packages look like graphic papers due to their printed surface. To improve these challenging classification tasks, sensors for mechanical properties of the paper objects can make an important contribution. In this paper, an automatic sensor is presented which can measure force characteristics when penetrating paper objects with specially shaped measuring tips. These force characteristics show good correlations to the mechanical properties determined in the laboratory according to standardized methods.

Background: Contrast-enhanced computed tomography (CECT) provides much more information compared to non-enhanced CT images, especially for the differentiation of malignancies, such as liver carcinomas. Contrast media injection phase information is usually missing on public datasets and not standardized in the clinic even in the same region and language. This is a barrier to effective use of available CECT images in clinical research., Purpose: The aim of this study is to detect contrast media injection phase from CT images by means of organ segmentation and machine learning algorithms., Methods: A total number of 2509 CT images split into four subsets of non-contrast (class #0), arterial (class #1), venous (class #2), and delayed (class #3) after contrast media injection were collected from two CT scanners. Seven organs including the liver, spleen, heart, kidneys, lungs, urinary bladder, and aorta along with body contour masks were generated by pre-trained deep learning algorithms. Subsequently, five first-order statistical features including average, standard deviation, 10, 50, and 90 percentiles extracted from the above-mentioned masks were fed to machine learning models after feature selection and reduction to classify the CT images in one of four above mentioned classes. A 10-fold data split strategy was followed. The performance of our methodology was evaluated in terms of classification accuracy metrics., Results: The best performance was achieved by Boruta feature selection and RF model with average area under the curve of more than 0.999 and accuracy of 0.9936 averaged over four classes and 10 folds. Boruta feature selection selected all predictor features. The lowest classification was observed for class #2 (0.9888), which is already an excellent result. In the 10-fold strategy, only 33 cases from 2509 cases (∼1.4%) were misclassified. The performance over all folds was consistent., Conclusions: We developed a fast, accurate, reliable, and explainable methodology to classify contrast media phases which may be useful in data curation and annotation in big online datasets or local datasets with non-standard or no series description. Our model containing two steps of deep learning and machine learning may help to exploit available datasets more effectively., (© 2024 The Authors. Medical Physics published by Wiley Periodicals LLC on behalf of American Association of Physicists in Medicine.)

In this paper, we put in dialogue the local dimension of a nation-state with the global challenges faced by universities worldwide. We focus on the case of Denmark, a nation that was exceptionally active in implementing international university reforms and where digitalisation is a high priority of the public sector governance. The article seeks to contribute to speculative research and critical studies by presenting a mixed-methods study that explores higher education teachers' views about desirable and realistic future scenarios in Denmark. The study draws on data from a survey applied at a large Danish university, analysed both quantitatively (N = 755) and qualitatively (N = 53). The findings show that teachers share clusters of concern about uncontrolled digitalisation and teaching automation, commodification of education, and modularisation of university courses, the latter being a contested but realistic perspective in the latest political reforms. They reaffirm the mission of the university as preparing students for solving real problems and contributing to the challenges of the present time. The paper concludes with a call for university management to recalibrate future imaginaries to the values expressed by the teachers, and the university they wish to create.

This paper proposes a Project-based team instruction methodology with open-ended projects to teach students critical analysis, design and implementation steps of developing Robotic Process Automation (RPA) for information systems. The use of project-based learning is appropriate for teaching RPA analysis and design with lean Six Sigma tools because of its experimental approach and documentation of logical steps needed to learn how to implement RPA successfully. The approach systematically documents work currently performed and defines future actions of the process while ensuring significant benefits are achieved with the RPA enhanced process. This methodology is important because the application of RPA is not commonly taught in Management Information System (MIS) programs. MIS students may not understand the significance of combined methodology, RPA tool, and usefulness of RPA until they enter the workforce where RPA is rapidly becoming available and easier to implement. The lecture sessions and exercises are valuable because it is easy to communicate the value of RPA in terms of time, quality, volume of transactions, etc. using Lean Six Sigma analytic approaches. The exercises involve hands on activities to make this learning experience interesting for students to readily associate the theoretical process improvement agreement and visualize the practical value of RPA enhanced projects. The paper discusses the need for process changes (and new development approaches) in organization to match the properties and functions within enterprise systems and ERPs that has led to criticism of the enterprise systems. This criticism is attributable to the ERPs' many subfunctions and operations that have limited adaptability and reduced functional and operational flexibility. The RPAs require limited prior knowledge of ERPs or their sub-processes for the improvements that are made in the performance of the organization. Thus, students do not have to "learn" how these enterprise or ERP systems operate to make changes or task improvements. This paper presents a project-based methodology and design approach focusing on development of RPAs that help students learning how to make the improvements using the RPS tools. The students learn that projects can deliver significant and tangible benefits to organizations while engaging students in key activities of the analysis, design and development process from a low code-no code perspective.

Pancreatic cancer remains one of the most challenging malignancies to diagnose and treat effectively, resulting in poor patient outcomes due to late-stage detection and limited therapeutic options. The emergence of biosensors has revolutionized cancer diagnosis and therapy, providing new avenues for early detection and personalized treatment. This paper explores the development and integration of biosensors within a unique expert system for pancreatic cancer diagnosis and drug delivery automation. It discusses the principles, types, and applications of biosensors in pancreatic cancer diagnosis, their role in automating drug delivery, and the design of an expert system that leverages these technologies to enhance patient outcomes. [ABSTRACT FROM AUTHOR]

Large language models (LLMs) are an exciting breakthrough in the rapidly growing field of artificial intelligence (AI), offering unparalleled potential in a variety of application domains such as finance, business, healthcare, cybersecurity, and so on. However, concerns regarding their trustworthiness and ethical implications have become increasingly prominent as these models are considered black-box and continue to progress. This position paper explores the potentiality of LLM from diverse perspectives as well as the associated risk factors with awareness. Towards this, we highlight not only the technical challenges but also the ethical implications and societal impacts associated with LLM deployment emphasizing fairness, transparency, explainability, trust and accountability. We conclude this paper by summarizing potential research scopes with direction. Overall, the purpose of this position paper is to contribute to the ongoing discussion of LLM potentiality and awareness from the perspective of trustworthiness and responsibility in AI. [ABSTRACT FROM AUTHOR]

This paper explores international students' engagement with educational technology for self-regulated English learning at an Australian university. Despite the increased use of automated feedback systems (AFSs) for language assessment, students' critical engagement with them for independent learning remains under-researched. The study primarily employed a qualitative approach to understand the students' preferred AFS tools and critical engagement throughout their personalised learning journeys but it also included a small-scale quantitative component. Data were gathered from seven students' e-portfolios, focus group interviews as well as a survey among 32 participants. Results highlight positive perceptions and successful use of AFSs, with students leveraging these tools to identify improvement areas, track progress and gain confidence. The study emphasises the importance of course structure, teacher guidance and a combination of human and automated feedback, in fostering learner autonomy and emotional self-regulation. The paper underscores the potential for sustained use of AFSs beyond the cours, and the significance of guiding learners to critically use these tools for ongoing learning and growth rather than dependence. These findings have significant implications, as readily available artificial intelligence tools such as ChatGPT hold great pedagogical potential for self-regulated learning within and beyond the language learning field.

The amount of grey literature and 'softer' intelligence from social media or websites is vast. Given the long lead-times of producing high-quality peer-reviewed health information, this is causing a demand for new ways to provide prompt input for secondary research. To our knowledge, this is the first review of automated data extraction methods or tools for health-related grey literature and soft data, with a focus on (semi)automating horizon scans, health technology assessments (HTA), evidence maps, or other literature reviews. We searched six databases to cover both health- and computer-science literature. After deduplication, 10% of the search results were screened by two reviewers, the remainder was single-screened up to an estimated 95% sensitivity; screening was stopped early after screening an additional 1000 results with no new includes. All full texts were retrieved, screened, and extracted by a single reviewer and 10% were checked in duplicate. We included 84 papers covering automation for health-related social media, internet fora, news, patents, government agencies and charities, or trial registers. From each paper, we extracted data about important functionalities for users of the tool or method; information about the level of support and reliability; and about practical challenges and research gaps. Poor availability of code, data, and usable tools leads to low transparency regarding performance and duplication of work. Financial implications, scalability, integration into downstream workflows, and meaningful evaluations should be carefully planned before starting to develop a tool, given the vast amounts of data and opportunities those tools offer to expedite research.

We conducted a systematic literature review on automated grading and feedback tools for programming education. We analysed 121 research papers from 2017 to 2021 inclusive and categorised them based on skills assessed, approach, language paradigm, degree of automation, and evaluation techniques. Most papers assess the correctness of assignments in object-oriented languages. Typically, these tools use a dynamic technique, primarily unit testing, to provide grades and feedback to the students or static analysis techniques to compare a submission with a reference solution or with a set of correct student submissions. However, these techniques' feedback is often limited to whether the unit tests have passed or failed, the expected and actual output, or how they differ from the reference solution. Furthermore, few tools assess the maintainability, readability, or documentation of the source code, with most using static analysis techniques, such as code quality metrics, in conjunction with grading correctness. Additionally, we found that most tools offered fully automated assessment to allow for near-instantaneous feedback and multiple resubmissions, which can increase student satisfaction and provide them with more opportunities to succeed. In terms of techniques used to evaluate the tools' performance, most papers primarily use student surveys or compare the automatic assessment tools to grades or feedback provided by human graders. However, because the evaluation dataset is frequently unavailable, it is more difficult to reproduce results and compare tools to a collection of common assignments.

It is well-known that digital learning materials influence the classroom curriculum and didactics. At the same time, few studies examine the role of the data visualizations offered by digital learning materials. Data visualizations signpost the emergence of students as data subjects who can be observed and compared on a computer screen. They thus shape teachers' socio-technical ways of seeing student learning and are likely to influence student assessments. Drawing inspiration from Science and Technology Studies (STS), this paper advances an analytical framework for eliciting the politics of data visualizations, focusing on automation, agencies, and aesthetics. The framework is exemplified through a case study of a popular Danish digital mathematics platform. In a concluding discussion, the paper considers the importance of addressing unsettled and ethical questions about the role of automated digital systems in teachers' knowledge practices.

Ensuring equity in education is a goal for sustainable development. Among the factors that hinder equity, socioeconomic status (SES) has the highest impact on learning Mathematics. This paper addresses the issue of equity at the secondary school level by proposing an approach based on adopting automatic formative assessment (AFA). Carefully designed mathematical activities with interactive feedback were experimented with a sample of 299 students of grade 8 for a school year. A control group of 257 students learned the same topics using traditional methodologies. Part of the sample belonged to low SES. The learning achievement was assessed through pre-and post-tests to understand if the adoption of AFA impacted learning and whether the results depended on the students' SES. The results show a positive effect of the experimentation (effect size: 0.42). Moreover, the effect size of the experimentation restricted to the low-SES group is high (0.77). In the treatment group, the results do not depend on SES, while in the control group, they do, suggesting that AFA is an equitable approach while traditional instruction risks perpetuating inequalities.

Purpose: Chatbots and artificial intelligence (AI) have the potential to alleviate some of the challenges faced by humans. Faculties frequently swamped with teaching and research may find it difficult to act in a parental role for students by offering them individualized advice. Hence, the primary purpose of this study is to review the literature on chatbots and AI in light of their role in auto-advising systems. The authors aimed to gain insights into the most pertinent topics and concerns related to robo academic advisor and identify any gaps in the literature that could serve as potential avenues for further research. Design/methodology/approach: The research employs a systematic literature review and bibliometric techniques to find 67 primary papers that have been published between 1984 and 2023. Using the Scopus database, the researchers built a summary of the literature on chatbots and AI in academic advice. Findings: Chatbot applications can be a promising approach to address the challenges of balancing personalized student advising with automation. More empirical research is required, especially on chatbots and other AI-based advising systems, to understand their effectiveness and how they can be integrated into educational settings. Research limitations/implications: This research's sample size may restrict its findings' generalizability. Furthermore, the study's focus on chatbots may overlook the potential benefits of other AI technologies in enhancing robo academic advising systems. Future research could explore the impact of robo academic advisors in diverse societal backgrounds to gain a more comprehensive understanding of their implications. Practical implications: Higher educational institutions (HEIs) should establish a robo academic advising system that serves various stakeholders. The system's chatbots and AI features must be user-friendly, considering the customers' familiarity with robots. Originality/value: This study contributes to a better understanding of HEIs' perceptions of the adoption of chatbots and AI in academic advising by providing insightful information about the main forces behind robo academic advising, illuminating the most frequently studied uses of chatbots and AI in academic advising.

Artificial intelligence (AI) and automation are newly impacting the governance of international students, a temporary resident category significant for both direct economic contributions and the formation of a "pool" of potential future immigrants in many immigrant-dependent countries. This paper focuses on tensions within Canada's education-migration ("edugration") system as new technologies intersect with migration regimes, which in turn relate to broader issues of security, administrative burdens, migration governance, and border imperialism. Using an Accidental Ethnography (AccE) approach drawing from practitioner-based legal research, we discuss three themes: (1) "bots at the gate" and the guise of AI's objectivity; (2) a murky international edu-tech industry; and (3) the administrative burdens of digitalized application systems. We suggest that researchers, particularly in education, can benefit from the insights of immigration practitioners who often become aware of potential trends before those less embedded in the everyday negotiation of migration governance.

Institutional discovery environments now serve as central resource databases for researchers in the academic environment. Over the last several decades, there have been numerous discovery layer research inquiries centering primarily on user satisfaction measures of discovery system effectiveness. This study focuses on the creation of a largely automated method for evaluating discovery layer quality, utilizing the bibliographic sources from student research projects. Building on past research, the current study replaces a semiautomated Excel Fuzzy Lookup Add-In process with a fully scripted R-based approach, which employs the stringdist R package and applies the Jaro-Winkler distance metric as the matching evaluator. The researchers consider the error rate incurred by relying solely on an automated matching metric. They also use Open Refine for normalization processes and package the tools together on an OSF site for other institutions to use. Since the R-based approach does not require special processing or time and can be reproduced with minimal effort, it will allow future studies and users of our method to capture larger sample sizes, boosting validity. While the assessment process has been streamlined and shows promise, there remain issues in establishing solid connections between research paper bibliographies and discovery layer use. Subsequent research will focus on creating alternatives to paper titles as search proxies that better resemble genuine information-seeking behavior and comparing undergraduate and graduate student interactions within discovery environments. [ABSTRACT FROM AUTHOR]

Increased levels of digitalisation present major opportunities for efficiency in the oil and gas industry but can also contribute to new risks and vulnerabilities. Based on developments in the industry, the Norwegian Ocean Industry Authority (HAVTIL) has in recent years pursued targeted knowledge development and follow-up of company's digitalisation initiatives. This paper explores data collected through HAVTIL's audits of the development and use of automated systems within well operations. The analysis of the data resulted in the identification of five main topics related to the implementation of digital technologies. The five main topics were organisational complexity, follow-up and implementation of technology, analysis and documentation, user-interface and alarms and competence and training. Overall, the results support research findings within human factors and technology development, pointing out that there is a lack of focus on human factors in both development projects and in operations. In addition, this paper provides insight into how digitalisation initiatives are followed-up and explores the results from the analysis in light of the current developments in the industry.

Background: Cardiovascular disease is the most common cause of death worldwide, including infection and inflammation related conditions. Multiple studies have demonstrated potential advantages of hybrid positron emission tomography combined with computed tomography (PET/CT) as an adjunct to current clinical inflammatory and infectious biochemical markers. To quantitatively analyze vascular diseases at PET/CT, robust segmentation of the aorta is necessary. However, manual segmentation is extremely time-consuming and labor-intensive., Purpose: To investigate the feasibility and accuracy of an automated tool to segment and quantify multiple parts of the diseased aorta on unenhanced low-dose computed tomography (LDCT) as an anatomical reference for PET-assessed vascular disease., Methods: A software pipeline was developed including automated segmentation using a 3D U-Net, calcium scoring, PET uptake quantification, background measurement, radiomics feature extraction, and 2D surface visualization of vessel wall calcium and tracer uptake distribution. To train the 3D U-Net, 352 non-contrast LDCTs from (2-[ 18 F]FDG and Na[ 18 F]F) PET/CTs performed in patients with various vascular pathologies with manual segmentation of the ascending aorta, aortic arch, descending aorta, and abdominal aorta were used. The last 22 consecutive scans were used as a hold-out internal test set. The remaining dataset was randomly split into training (n = 264; 80%) and validation (n = 66; 20%) sets. Further evaluation was performed on an external test set of 49 PET/CTs. The dice similarity coefficient (DSC) and Hausdorff distance (HD) were used to assess segmentation performance. Automatically obtained calcium scores and uptake values were compared with manual scoring obtained using clinical softwares (syngo.via and Affinity Viewer) in six patient images. intraclass correlation coefficients (ICC) were calculated to validate calcium and uptake values., Results: Fully automated segmentation of the aorta using a 3D U-Net was feasible in LDCT obtained from PET/CT scans. The external test set yielded a DSC of 0.867 ± 0.030 and HD of 1.0 [0.6-1.4] mm, similar to an open-source model with a DSC of 0.864 ± 0.023 and HD of 1.4 [1.0-1.8] mm. Quantification of calcium and uptake values were in excellent agreement with clinical software (ICC: 1.00 [1.00-1.00] and 0.99 [0.93-1.00] for calcium and uptake values, respectively)., Conclusions: We present an automated pipeline to segment the ascending aorta, aortic arch, descending aorta, and abdominal aorta on LDCT from PET/CT and to accurately provide uptake values, calcium scores, background measurement, radiomics features, and a 2D visualization. We call this algorithm SEQUOIA (SEgmentation, QUantification, and visualizatiOn of the dIseased Aorta) and is available at https://github.com/UMCG-CVI/SEQUOIA. This model could augment the utility of aortic evaluation at PET/CT studies tremendously, irrespective of the tracer, and potentially provide fast and reliable quantification of cardiovascular diseases in clinical practice, both for primary diagnosis and disease monitoring., (© 2024 The Authors. Medical Physics published by Wiley Periodicals LLC on behalf of American Association of Physicists in Medicine.)

This paper examines the feasibility of incorporating visual cueing systems within vehicles to mitigate the risk of experiencing motion sickness. The objective is to enhance passenger awareness and the ability to anticipate the forces associated with car travel motion. Through a comprehensive literature review, the findings demonstrate that visual cues can mitigate motion sickness for particular in-vehicle configurations, whereas their influence on situational awareness is not clear yet. Each type of visual cue proved more effective when presented in the peripheral field of view rather than solely in the central vision. Promising applications can be found within interactive screens and ambient lighting, while the use of extended reality shows potential for future investigations. In addition, integrating such systems into highly automated vehicles shows potential to improve their overall user acceptance.

Nishijin is famous for its traditional technique of cutting Japanese paper to a thickness of 0.2 to 0.3 mm and weaving it with warp and weft threads. This technique is called ' hikibaku '. In the previous paper, we reported on the creation of a new textile with a new tactile feeling by using natural cowhide instead of Japanese paper used in hikibaku. In this paper, we describe the details of the automation of the hikibaku technique that created the new textile, and conduct a durability test of the newly produced textile using artificial leather, which is of stable quality, instead of natural cowhide, and discuss its new applications. [ABSTRACT FROM AUTHOR]