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Despite widespread adoption and maturity, paper persistence endures in many Electronic Health Record (EHR) systems, particularly for complex workflows involving multiple steps from different stakeholders separated in time. In our health system, Latent Tuberculosis Infection (LTBI) testing was one such workflow where a Tuberculin Skin Test (TST) must be administered and then correctly read 48-72 hours later and documented. This paper discusses a low-resource workflow analysis and clinical decision support approach to replace a paper workflow and garner the benefits of the EHR for clearer documentation and retrieval of LTBI results. Our approach resulted in a significant increase in completed TST documentation, 57% (24/42) to 95% (18/19), P < 0.003. Human-centered design practices such as work system analysis and formative usability testing are feasible with limited resources and improve the likelihood of success of electronic workflows by designing solutions that fit existing clinical workflows and automating processes wherever possible.

Digital Health Twins (DHTs) hold immense potential to revolutionize healthcare by providing personalized virtual models of individual patients and their health conditions. This position paper discusses about the needs to overcoming the challenges in standardization and information models for data integration, collection, and visualization for realizing the full potential with digital twin technology. By addressing these challenges, digital health twins can enable better decision-making, enhance patient care, and contribute to the advancement of personalized healthcare solutions.

The verdict of the UK Supreme Court in the case of Bellman versus Boojum-Snark Integrated Care Trust (2027) will have profound implications for medical practice, medical education, and medical research, as well as the regulation of medicine and allied healthcare fields. Major changes will result from the definition of person-centred care built into the expanded definition of informed and preference-based consent central to the judgment made in favour of Bellman's negligence claim. (For the avoidance of doubt this is a vision paper.).

Background: Telehealth and mHealth apps become increasingly popular in health professions such as physiotherapy calling for increased awareness on functionality, privacy, and data security., Objectives: This work presents a functionality, privacy, and data-security evaluation of four telehealth services commonly used in physiotherapy., Methods: We examined functionality and features, data protection, privacy implementations and data-security with a questionnaire and performed an in-depth investigation of the services., Results: Privacy and security relevant findings such as use of outdated webservers, problems with certificate renewal as well as questionable GDPR compliance were reported., Conclusion: Due to the privacy and security relevant findings in this analysis it can be concluded that there is a need for improvement in design, development, operation as well as regulation of telehealth apps and services.

Traditionally, Electronic Medical Records (EMR) have been designed to mimic paper records. Organizing and presenting medical information along the lines that evolved for non-digital records over the decades, reduced change management for medical users, but failed to make use of the potential of organizing digital data. We proposed a method to create clinical dashboards to increase the usability of information in the medical records. Official clinical guidelines were studied by a working group, including dashboard target users. Necessary clinical concepts contained in the medical records were identified according to the clinical context and finally, dedicated technical tools with standard terminologies were used to represent categories of information. We used this method to generate and implement a dashboard for sepsis. The dashboard was found to be appropriate and easy to use by the target users.

The 2023 edition of the AIxIA Conference, held in Rome, brought together a large number of researchers and practitioners to discuss the most recent and important advancements in Artificial Intelligence (AI). The conference featured 19 workshops, organized by 77 experts, attracting 248 submissions and resulting in 16 proceedings. This special issue presents extended versions of selected papers initially showcased at these workshops. Each paper underwent rigorous review and represents a diverse array of topics, reflecting the multifaceted nature of the Italian AI community. The topics covered include ethical foundations to symbiotic AI, symbolic knowledge extraction from black-box models, creative influence prediction using graph theory, AI approaches to multidimensional poverty prediction, an assessment of AI-based supports for informal caregivers, deep learning-based EEG denoising, AI-assisted board-game-based learning, large language models for assessment and feedback in higher education, geometric reasoning in the Traveling Salesperson Problem, defeasible reasoning in weighted knowledge bases, and conditional computation in neural networks. These contributions demonstrate the innovative and interdisciplinary research within the AI community, offering valuable insights and advancing the field. [ABSTRACT FROM AUTHOR]

The aim of the work is to assess the environmental impacts of Ultra High Frequency RFID tags. Through a Life Cycle Assessment approach, two case studies have been investigated, namely a standard plastic and a paper-based tags. Primary data on tags' components, manufacturing and transportation were collected, while secondary data for the raw materials processing and tags' end of life were retrieved. The Recipe Midpoint method was used to evaluate the impacts. Results show that, for both tags, the greatest contributions to global warming, terrestrial acidification, mineral and fossil resource scarcity are due to raw material extraction (more than 50%) and manufacturing phase (30–50%), which resulted impactful also on the ionizing radiation (70%). Interestingly, the paper tag allows to save up to 23% of the greenhouse gas emissions and decreases the impact on the above-mentioned categories, resulting the eco-friendly option. The conclusion of the work contributes to update the scientific literature, still poor in RFID environmental evaluations, and are useful for researchers interested in comparing the traditional handling systems' impacts to the RFID scenario. Furthermore, the outcomes will be used as input for subsequent research, aimed at developing a tool to measure the return on the environment of RFID deployments. [ABSTRACT FROM AUTHOR]

Background: Multi-arm, multi-stage (MAMS) platform trials can accelerate the identification of disease-modifying treatments for Parkinson's disease (PD) but there is no current consensus on the optimal outcome measures (OM) for this approach. Objective: To provide an up-to-date inventory of OM for disease-modifying PD trials, and a framework for future selection of OM for such trials. Methods: As part of the Edmond J Safra Accelerating Clinical Trials in Parkinson Disease (EJS ACT-PD) initiative, an expert group with Patient and Public Involvement and Engagement (PPIE) representatives' input reviewed and evaluated available evidence on OM for potential use in trials to delay progression of PD. Each OM was ranked based on aspects such as validity, sensitivity to change, participant burden and practicality for a multi-site trial. Review of evidence and expert opinion led to the present inventory. Results: An extensive inventory of OM was created, divided into: general, motor and non-motor scales, diaries and fluctuation questionnaires, cognitive, disability and health-related quality of life, capability, quantitative motor, wearable and digital, combined, resource use, imaging and wet biomarkers, and milestone-based. A framework for evaluation of OM is presented to update the inventory in the future. PPIE input highlighted the need for OM which reflect their experience of disease progression and are applicable to diverse populations and disease stages. Conclusion: We present a range of OM, classified according to a transparent framework, to aid selection of OM for disease-modifying PD trials, whilst allowing for inclusion or re-classification of relevant OM as new evidence emerges. [ABSTRACT FROM AUTHOR]

BACKGROUND: Workplace inspections are applied to facilitate the adherence to the occupational health and safety regulations. The Iranian Ministry of Health introduced a new software system for tablets to inspect workplaces. OBJECTIVES: The aim of this study was to take measurements of the usability, mental workload, and mood of inspectors. METHODS: Inspectors used both pen-and-paper and tablet methods to inspect the automotive industry in a mixed-method procedure. The NASA-TLX score, QUIS score, I-PANAS (SF) situation, inspection time, and number of errors were collected throughout the procedure. The differences were investigated using a paired sample and the Wilcoxon signed ranks test. RESULTS: In terms of efficacy, using the tablet resulted in lower error rates, but it took longer to complete the inspection task (P < 0.001). Participants perceived a lower workload when inspecting with a tablet rather than the traditional method. (Mental Demand: p < 0.002, Performance: p < 0.009, Effort: p < 0.012, TLX: p < 0.002 based on various subcomponents of NASA-TLX). The newly introduced system's usability was insufficient. CONCLUSION: Although the use of tablets has improved safety inspections, ergonomic redesign of the system and consideration of a user-centered approach, as well as inspector training, can make the system more likely to succeed. [ABSTRACT FROM AUTHOR]

The text is an introduction to the selected papers from the HES-2023 conference, which focuses on the application of electromagnetic fields for materials processing. The conference was postponed by a year due to the pandemic and the war in Ukraine. The main developments in electroheat involve a multi-physics approach to modeling and simulation, with a focus on coupling different physical domains. The use of AI-based methods, such as digital twins and machine learning, in electrothermal systems is a current topic of research. The conference also covered topics such as induction heating, induction hardening, and inverse problems in induction heating. Overall, 52 papers were presented at the conference, and the 10 selected papers cover the main topics discussed. [Extracted from the article]

The fabrication of a fully bio-sourced adsorbent of Cd(II) by covalent immobilization of quinine on cellulose paper is described. The double bond of commercially available quinine was converted to a terminal alkyne function which was reacted with cellulose paper, chemically modified with azide functions, through a 1,3-dipolar cycloaddition, leading to Cell-Quin. The adsorption efficiency of Cell-Quin was investigated to determine the optimal pH, contact time and dose of adsorbent, ultimately leading to high levels of removal. The mechanism of adsorption of Cell-Quin was deeply rationalized through kinetic experiments and isotherm modeling. We also showed that Cell-Quin could adsorb other heavy metals such as Cu(II), Pb(II), Ni(II) and Zn (II)., (Copyright © 2019. Published by Elsevier B.V.)

As more people express their thoughts on products on various online shopping platforms, the feelings expressed in these opinions are becoming a significant source of information for marketers and buyers. These opinions have a big impact on consumers' decision to buy the best quality product. When there are too many features or a small number of records to analyze, the decision-making process gets difficult. A recent stream of study has used the conventional quantitative star score ratings and textual content reviews in this context. In this research, a decision-making framework is proposed that relies on feature-based opinions to analyze the textual content of reviews and classify buyer's opinions, thereby assisting consumers in making long-term purchases. The framework is proposed in this paper for product purchase decision making based on feature-based opinions and deep learning. Framework consists of four components: i) Pre-processing, ii) Feature extraction, iii) Feature-based opinion classification, and iv) Decision-making. Web scraping is used to obtain the dataset of Smartphone reviews, which is subsequently clean and pre-processed using tokenization and POS tagging. From the tagged dataset, noun labeled words are retrieved, and then the probable product's features are extracted. These feature-based sentences or reviews are processed using a word embedding to generate review vectors that identify contextual information. These word vectors are used to construct hidden vectors at the word and sentence levels using a hierarchical attention method. With respect to each feature, reviews are divided into five classes: extremely positive, positive, extremely negative, negative, and neutral. The proposed method may readily detect a customer's opinion on the quality of a product based on a certain attribute, which is beneficial in making a purchase choice. [ABSTRACT FROM AUTHOR]

A "Big Data" approach was used in part to study the use of printed paper in a 700 bed paperless hospital, in part to study the usefulness of the approach. Between 1,2 and 1,5 million pages were printed each month, corresponding to 10% of a citizens' monthly paper use. The identified use of printed paper did not seem to be high compared to other organisations. The big data approach was not able to answer all our questions, primarily because we did not get log data for the source programmes for the printing. The approach could consequently not provide the data needed to reduce paper printing.

The number of scientific papers has been increasing ever more rapidly. Researchers have to spend a lot of time classifying papers relevant to their study, especially into fine-grained subfields. However, almost all existing paper classification models are coarse-grained, which can not meet the needs of researchers. Observing this, we propose a lightweight fine-grained classification model for scientific paper. Dynamic weighting coefficients on feature words are incorporated into the model to improve the classification accuracy. The feature word weight is optimized by the Mean Decrease Accuracy (MDA) algorithm. Considering applicability, the lightweight processing is conducted through algorithm pruning and training sample pruning. Comparison with mainstream models shows simultaneous improvement in accuracy and time efficiency by our model. [ABSTRACT FROM AUTHOR]

Recovery of cellulose fibres from paper mill effluent has been studied using common polysaccharides or biopolymers such as Guar gum, Xanthan gum and Locust bean gum as flocculent. Guar gum is commonly used in sizing paper and routinely used in paper making. The results have been compared with the performance of alum, which is a common coagulant and a key ingredient of the paper industry. Guar gum recovered about 3.86mg/L of fibre and was most effective among the biopolymers. Settling velocity distribution curves demonstrated that Guar gum was able to settle the fibres faster than the other biopolymers; however, alum displayed the highest particle removal rate than all the biopolymers at any of the settling velocities. Alum, Guar gum, Xanthan gum and Locust bean gum removed 97.46%, 94.68%, 92.39% and 92.46% turbidity of raw effluent at a settling velocity of 0.5cm/min, respectively. The conditions for obtaining the lowest sludge volume index such as pH, dose and mixing speed were optimised for guar gum which was the most effective among the biopolymers. Response surface methodology was used to design all experiments, and an optimum operational setting was proposed. The test results indicate similar performance of alum and Guar gum in terms of floc settling velocities and sludge volume index. Since Guar gum is a plant derived natural substance, it is environmentally benign and offers a green treatment option to the paper mills for pulp recycling., (Copyright © 2014. Published by Elsevier B.V.)

To enable a quick and accurate access of targeted scientific and technological literature from massive stocks, here a deep content-based collaborative filtering method, namely DeepCCF, for personalized scientific and technological literature resources recommendation was proposed. By combining content-based filtering (CBF) and neural network-based collaborative filtering (NCF), the approach transforms the problem of scientific and technological literature recommendation into a binary classification task. Firstly, the word2vec is used to train the words embedding of the papers' titles and abstracts. Secondly, an academic literature topic model is built using term frequency–inverse document frequency (TF-IDF) and word embedding. Thirdly, the search and view history and published papers of researchers are utilized to construct the model that portrays the interests of researchers. Deep neural networks (DNNs) are then used to learn the nonlinear and complicated high-order interaction features between users and papers, and the top k recommendation list is generated by predicting the outputs of the model. The experimental results show that our proposed method can quickly and accurately capture the latent relations between the interests of researchers and the topics of paper, and be able to acquire the researchers' preferences effectively as well. The proposed method has tremendous implications in personalized academic paper recommendation, to propel technological progress. [ABSTRACT FROM AUTHOR]

BACKGROUND: External focus isometric exercises using a paper balloon can change trunk muscle activation in the chest squeeze; however, it is unknown whether this method affects muscle activities in conventional exercises. OBJECTIVE: To check variations of trunk muscle activity during front plank (static task) and shoulder press (dynamic task) both with and without instruction to avoid crushing an object. METHODS: Twenty-six healthy adult males aged 19–49 were recruited. Ten trunk muscle activities were measured using surface electromyography during a front plank and dynamic shoulder press exercises, both with and without external-focus instruction. RESULTS: Adding the external-focus using the paper balloon to the front plank significantly activated 8 out of the 10 muscles. In the downward shoulder press, 5 out of 10 muscles with 50% 1 RM, 2 out of 10 muscles with 100% 1 RM were significantly activated. CONCLUSIONS: Adding external-focus instruction using paper balloon increases trunk muscles in front plank and shoulder press while possibly improving trunk stability. Novel exercises using paper balloon may efficiently activate specific muscles without external loading thus possibly reducing the stress on the involved joints during exercise. [ABSTRACT FROM AUTHOR]