User's Guide Research Articles

Extending the Reporting Items of Practice Guidelines in Healthcare (RIGHT) statement for reporting Chinese patent medicine guidelines: a protocol

Background: Based on the main document of the Reporting Items of Practice Guidelines in Healthcare (RIGHT) statement, this study will aim to develop the reporting checklist applicable to the guidelines of Chinese patent medicine (CPM). Methods: According to the methodology recommended by the Enhancing the QUAlity and Transparency Of health Research (EQUATOR) network, three well-defined working groups were established, namely the coordination team, the advisory group, and the Delphi panel. The coordination team conducted a literature analysis of the already published CPM guidelines, the extensions of the RIGHT statement, and CPM related reporting specifications to initially form an initial item pool. The Delphi panel of multidisciplinary backgrounds will conduct two rounds of Delphi questionnaires to identify an extension item for RIGHT for CPM. These questionnaires will be sent to each Delphi panel member via email, and each member will independently fill out and provide comments within 10 working days of receiving the questionnaire. Finally, feedback will be provided to the coordination team and the post efficacy assessment of the already published clinical practice guidelines for CPM to determine the final items for RIGHT for CPM checklist. Dissemination: The extended version of the RIGHT for CPM checklist will be published in a peer reviewed journal. Conclusion: We hope that the upcoming RIGHT for CPM checklist provides detailed guidance for developers and users of CPM guidelines, further improving the quality of reports, and promoting the effective dissemination and implementation of CPM guidelines.

Molecular diagnostic strategies and management of dust mite allergy

Dust mites are one of the most important allergens, widely distributed around the world, especially in household environments. Dermatophagoides pteronyssinus, Dermatophagoides farinae and Blomia tropicalis are the most common species of dust mites. There are more than 35 known sensitization components of dust mites, among which Der p 1, Der p 2 and Der p 23 are the major components. Clinically, allergen skin test and serum specific immunoglobulin E (sIgE) detection are widely used in the preliminary diagnosis of dust mite allergy. However, these methods cannot accurately identify specific dust mite sensitization components. Considering that there are significant differences in the allergenic components of dust mites in different regions and populations, component-resolved diagnosis of dust mite is particularly important in accurately determining the allergenic components. This is not only of guiding significance for allergen avoidance, but also important for determining the immunotherapy regimen for dust mites. In order to strengthen the understanding of the molecular diagnosis of dust mites and promote the integration of allergy science in China with the international standards, this article interprets the "Allergy Molecular Allergology User's Guide 2.0" published recently by the European Academy of Allergy and Clinical Immunology.

The Role of Social Media in Online Marketing of Halal Products

This study undertakes a descriptive analysis focused on elucidating the pivotal role of social media in the realm of online business marketing. The primary goal is to furnish valuable insights and practical guidance for internet users engaging in the marketing of their online businesses through the utilization of social media platforms. The chosen research methodology for this study is descriptive qualitative. This method is particularly well-suited for accommodating the myriad influences on value patterns, presenting data in the form of words and image. The findings of this research endeavor are anticipated to offer a nuanced understanding of the dynamics at play, shedding light on the various dimensions of leveraging social media effectively for halal online business marketing. Ultimately, the study aspires to equip halal business practitioners with actionable insights to enhance their online marketing endeavors in the ever-evolving landscape of social media

Mostly Automated Verification of Liveness Properties for Distributed Protocols with Ranking Functions

Distributed protocols have long been formulated in terms of their safety and liveness properties. Much recent work has focused on automatically verifying the safety properties of distributed protocols, but doing so for liveness properties has remained a challenging, unsolved problem. We present LVR, the first framework that can mostly automatically verify liveness properties for distributed protocols. Our key insight is that most liveness properties for distributed protocols can be reduced to a set of safety properties with the help of ranking functions. Such ranking functions for practical distributed protocols have certain properties that make them straightforward to synthesize, contrary to conventional wisdom. We prove that verifying a liveness property can then be reduced to a simpler problem of verifying a set of safety properties, namely that the ranking function is strictly decreasing and nonnegative for any protocol state transition, and there is no deadlock. LVR automatically synthesizes ranking functions by formulating a parameterized function of integer protocol variables, statically analyzing the lower and upper bounds of the variables as well as how much they can change on each state transition, then feeding the constraints to an SMT solver to determine the coefficients of the ranking function. It then uses an off-the-shelf verification tool to find inductive invariants to verify safety properties for both ranking functions and deadlock freedom. We show that LVR can mostly automatically verify the liveness properties of several distributed protocols, including various versions of Paxos, with limited user guidance.

The Study of Copyright Infringement Liability of Generative Artificial Intelligence

As AI continues to produce content at an unprecedented rate, it is essential to establish a robust legal framework to protect the rights of creators and appropriately assign liability in cases of copyright infringement. Generative AI technology has the characteristics of high data demand, strong human-computer interaction, weak interpretability, and weak stability. The main civil subjects in generative AI services are generative AI service providers and users. In the pre-training stage, if generative AI uses copyrighted works without authorization, it should be recognized as copyright infringement, and the relevant infringement liability should be borne by generative AI service providers. In the content generation stage, the high similarity between AI-generated content and prior works can be attributed to various factors, including flaws in the generative model, intentional design choices, and user input and guidance. Both the generative AI service provider and the user bear direct tort liability for the infringement. However, the question of whether generative AI service providers also bear indirect tort liability should be explored, considering their role and similarities to traditional ISPs. Generative AI service providers should fulfill their obligations, take appropriate actions to prevent infringement, and report any illegal activities to the relevant authorities.

Technical note: A volumetric method for measuring the longitudinal arch of human tracks and feet.

Fossil footprints (i.e., tracks) were believed to document arch anatomical evolution, although our recent work has shown that track arches record foot kinematics instead. Analyses of track arches can thereby inform the evolution of human locomotion, although quantifying this 3-D aspect of track morphology is difficult. Here, we present a volumetric method for measuring the arches of 3-D models of human tracks and feet, using both Autodesk Maya and Blender software. The method involves generation of a 3-D object that represents the space beneath the longitudinal arch, and measurement of that arch object's geometry and spatial orientation. We provide relevant tools and guidance for users to apply this technique to their own data. We present three case studies to demonstrate potential applications. These include, (1) measuring the arches of static and dynamic human feet, (2) comparing the arches of human tracks with the arches of the feet that made them, and (3) direct comparisons of human track and foot arch morphology throughout simulated track formation. The volumetric measurement tool proved robust for measuring 3-D models of human tracks and feet, in static and dynamic contexts. This tool enables researchers to quantitatively compare arches of fossil hominin tracks, in order to derive biomechanical interpretations from them, and/or offers a different approach for quantifying foot morphology in living humans.

Nurses’ records on guidance for users with tuberculosis in Primary Health Care

Resumo Objetivo Avaliar a completude dos registros de enfermeiros sobre as orientações dadas a usuários com tuberculose (TB) acompanhados na Atenção Primária à Saúde (APS). Métodos Estudo documental e retrospectivo, cuja população foi constituída por todos prontuários dos casos novos de TB notificados em Unidades de Saúde da Família de um município da Paraíba entre 2015 e 2019. O estudo foi desenvolvido entre julho e setembro de 2020, com uma amostra de 190 prontuários selecionados por amostragem proporcional e probabilística sistemática. Utilizou-se o software R para análise estatística, com nível de significância de 5%, empregando estatística descritiva, Diagrama de Pareto e análise de tendência. Resultados Do total, 60% dos indicadores foram classificados como muito ruins (orientações sobre alimentação, tratamento e consultas) na avaliação da completude do registro sobre as orientações de enfermeiros a respeito das orientações à TB. O Diagrama de Pareto destacou as orientações relacionadas a alimentação e tratamento como os principais problemas no registro, representando mais de 50% da incompletude total. Os modelos de tendência apresentaram evidências estatísticas crescentes para a incompletude do indicador ‘orientações sobre consultas’ (p=0,016) e tendência crescente para os indicadores ‘orientações sobre exames e consultas’. Em contrapartida, as orientações sobre alimentação e exames mostraram tendência decrescente de incompletude. Conclusão Os registros mostraram uma completude inadequada, acompanhada por uma tendência crescente de incompletude no registro de orientações aos usuários com TB na APS. Esse cenário aponta para a urgência de medidas de intervenção na saúde pública, para aprimorar o acompanhamento da TB na APS.

Region-Aware Portrait Retouching With Sparse Interactive Guidance

Portrait retouching aims to improve the aesthetic quality of input portrait photos and especially requires human-region priority. The deep learning-based methods largely elevate the retouching efficiency and provide promising retouched results. However, existing portrait retouching methods focus on automatic retouching, which treats all human-regions equally and ignores users' preferences for specific individuals, thus suffering from limited flexibility in interactive scenarios. In this work, we emphasize the importance of users' intents and explore the interactive portrait retouching task. Specifically, we propose a region-aware retouching framework with two branches: an automatic branch and an interactive branch. The automatic branch involves an encoding-decoding process, which searches region candidates and performs automatic region-aware retouching without user guidance. The interactive branch encodes sparse user guidance into a priority condition vector and modulates latent features with a region selection module to further emphasize the user-specified regions. Experimental results show that our interactive branch effectively captures users' intents and generalizes well to unseen scenes with sparse user guidance, while our automatic branch also outperforms the state-of-the-art retouching methods due to improved region-awareness.

Conceptualizing Patient as an Organization With the Adoption of Digital Health.

This study aimed to identify the factors affecting adoption intention using the TAM (Technology Acceptance Model), HB (Health Belief model), and the UTAUT (Unified Theory of Acceptance and Use of Technology). The argument is made that the adoption of the technology enables patients to create resources (ie, data), transforming patients from mere consumers to producers as well. PLS analysis showed that health beliefs and perceived ease of use had positive effects on the perceived usefulness of digital healthcare, and system capabilities positively impacted perceived ease of use. Furthermore, perceived service, the customer's willingness to change and reference group influence significantly impacted adoption intention (b > 0.1, t > 1.96, P < .05). However, privacy protection and data security, online healthcare resources, and user guidance were not positively associated with perceived usefulness. Perceived usefulness, the customer's willingness to change, and the influence of the reference group are decisive variables affecting adoption intention among the general population, whereas privacy protection and data security are indecisive variables. Online resources and user guides do not support adoption intentions.

Challenges for Future Robotic Sorters of Mixed Industrial Waste: A Survey

To achieve recycling of mixed industrial waste toward an advanced sustainable society, waste sorting automation through robots is crucial and urgent. For this purpose, a robot is required to recognize the category, shape, pose, and condition of different waste items and manipulate them according to the category to be sorted. This survey considers three potential difficulties in the sorting automation: 1) End-effector: to robustly grasp and manipulate different waste items with dirt and deformations; 2) Sensor: to recognize the category, shape, and pose of existing objects to be manipulated and the wet and dirty conditions of their surfaces; and 3) Planner: to generate feasible and efficient sequences and trajectories. This survey includes 76 references to studies related to automatic waste sorting and 159 references to worldwide waste recycling attempts. This pioneering investigation reveals the possibility and limitations of conventional systems; thus, providing insights on open issues and potential technologies to achieve a robot-incorporated sorter for the chaotic mixed waste is one of its contributions. This paper further presents a system design policy for readers and discusses future advanced sorters, thereby contributing to the field of robotics and automation. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —Most automated sorting systems operate for limited target waste items. This study is motivated by the automation of mixed industrial waste treatment facilities using advanced robotic sorters. Emerging advances and increasing functionalities of robot system components will widen system applicability and increase use cases in the chaotic mixed industrial waste domain. This paper surveys the research conducted to date, discusses open issues and potential approaches, and presents user guides that provide practitioners with a system design policy. The user guides created according to the strengths and weaknesses of each system configuration provide future researchers and developers with a useful a priori design policy that has been thus far validated on efficiency, quality, productivity, and reliability. A question-and-answer style guide and a sorting-target-aware previous study reference list allows users to find the desired system configuration, including the investigated components according to their purpose.

Body MRI Pulse Sequences: Atlas and User Guide.

Body MRI Pulse Sequences: Atlas and User Guide.

A Model of Science, Technology, Engineering, and Mathematics Remote Research-Based Learning

Opportunities for research-based learning at the high school level are limited, and with the COVID-19 pandemic, these have been further reduced. Such opportunities are particularly scarce for authentic research experiences (AREs), which allow students to identify as scientists by collecting data that contributes to scientists’ research. In response to the COVID-19 pandemic, we adapted two of our AREs for classroom settings, as remote independent research experiences for students to conduct from home. User guides and protocols from the AREs, Genotype-to-Phenotype Research with Corn and Discover Volvox Development, were adapted to instruct high school students to work on their own with the guidance of scientists and ARE coordinators. These independent authentic research experiences (IAREs) were implemented in the summer of 2020 and have since been available to students. Student responses to reflection questions and the Laboratory Course Assessment Survey indicate that IAREs provide students with significant gains including learning science content and research practices, collaborating with scientists, facing and resolving challenges, and contributing to scientific research.

Selecting the Appropriate User Experience Questionnaire and Guidance for Interpretation: the UEQ Family

Selecting the Appropriate User Experience Questionnaire and Guidance for Interpretation: the UEQ Family

Beyond Correlation: Incorporating Counterfactual Guidance to Better Support Exploratory Visual Analysis.

Providing effective guidance for users has long been an important and challenging task for efficient exploratory visual analytics, especially when selecting variables for visualization in high-dimensional datasets. Correlation is the most widely applied metric for guidance in statistical and analytical tools, however a reliance on correlation may lead users towards false positives when interpreting causal relations in the data. In this work, inspired by prior insights on the benefits of counterfactual visualization in supporting visual causal inference, we propose a novel, simple, and efficient counterfactual guidance method to enhance causal inference performance in guided exploratory analytics based on insights and concerns gathered from expert interviews. Our technique aims to capitalize on the benefits of counterfactual approaches while reducing their complexity for users. We integrated counterfactual guidance into an exploratory visual analytics system, and using a synthetically generated ground-truth causal dataset, conducted a comparative user study and evaluated to what extent counterfactual guidance can help lead users to more precise visual causal inferences. The results suggest that counterfactual guidance improved visual causal inference performance, and also led to different exploratory behaviors compared to correlation-based guidance. Based on these findings, we offer future directions and challenges for incorporating counterfactual guidance to better support exploratory visual analytics.

Registros de enfermeiros sobre orientações aos usuários com tuberculose na Atenção Primária

Abstract Objective To evaluate the completeness of nurses’ records on guidance given to users with tuberculosis (TB) followed in Primary Health Care (PHC). Methods This was a documentary and retrospective study whose population consisted of all records of new TB cases reported in Family Health Units in a municipality (State of Paraíba) in the period 2015-2019. The study was conducted from July to September 2020 with a sample of 190 medical records selected by systematic proportional and probabilistic sampling. The R software for statistical analysis was used with a 5% significance level, employing descriptive statistics, Pareto Chart, and trend analysis. Results In assessing the completeness of the guidance record about nutrition, treatment, and consultations given by nurses for patients with TB, 60% of the indicators were classified as very poor. The Pareto Chart highlighted guidance on food and treatment as the main problems in the record, representing more than 50% of the total incompleteness. The trend models showed increasing statistical evidence for the incompleteness of the guidance on consultations indicator (p=0.016) and a growing statistical trend for the incompleteness of the guidance on exams and consultations indicators. In contrast, guidance on nutrition and exams showed a decreasing trend in incompleteness. Conclusion Records showed inadequate completeness accompanied by an increasing trend for incompleteness in the registration of guidance for users with TB in PHC. This scenario points to the urgency of intervention measures in Public Health to improve TB monitoring in PHC.

CAISeg: A Clustering-Aided Interactive Network for Lesion Segmentation in 3D Medical Imaging.

Accurate lesion segmentation in medical imaging is critical for medical diagnosis and treatment. Lesions' diverse and heterogeneous characteristics often present a distinct long-tail distribution, posing difficulties for automatic methods. Currently, interactive segmentation approaches have shown promise in improving accuracy, but still struggle to deal with tail features. This triggers a demand of effective utilizing strategies of user interaction. To this end, we propose a novel point-based interactive segmentation model called Clustering-Aided Interactive Segmentation Network (CAISeg) in 3D medical imaging. A customized Interaction-Guided Module (IGM) adopts the concept of clustering to capture features that are semantically similar to interaction points. These clustered features are then mapped to the head regions of the prompted category to facilitate more precise classification. Meanwhile, we put forward a Focus Guided Loss function to grant the network an inductive bias towards user interaction through assigning higher weights to voxels closer to the prompted points, thereby improving the responsiveness efficiency to user guidance. Evaluation across brain tumor, colon cancer, lung cancer, and pancreas cancer segmentation tasks show CAISeg's superiority over the state-of-the-art methods. It outperforms the fully automated segmentation models in accuracy, and achieves results comparable to or better than those of the leading point-based interactive methods while requiring fewer prompt points. Furthermore, we discover that CAISeg possesses good interpretability at various stages, which endows CAISeg with potential clinical application value.

Density functional theory methods applied to homogeneous and heterogeneous catalysis: a short review and a practical user guide.

The application of density functional theory (DFT) methods in catalysis has been growing fast in the last few decades thanks to both the availability of more powerful high computing resources and the development of new efficient approximations and approaches. DFT calculations allow for the understanding of crucial catalytic aspects that are difficult or even impossible to access by experiments, thus contributing to faster development of more efficient and selective catalysts. Depending on the catalytic system and properties under investigation, different approaches should be used. Moreover, the reliability of the obtained results deeply depends on the approximations involved in both the selected method and model. This review addresses chemists, physicists and materials scientists whose interest deals with the application of DFT-based computational tools in both homogeneous catalysis and heterogeneous catalysis. First, a brief introduction to DFT is presented. Then, the main approaches based on atomic centered basis sets and plane waves are discussed, underlining the main differences, advantages and limitations. Eventually, guidance towards the selection of the catalytic model is given, with a final focus on the evaluation of the energy barriers, which represents a crucial step in all catalytic processes. Overall, the review represents a rational and practical guide for both beginners and more experienced users involved in the wide field of catalysis.

A Mediated Moderation Analysis Of The Efficacy Of E-Hrm

The aim of this paper is to examine the effectiveness of e-HRM by a mediated moderation analysis. This paper used both descriptive and quantitative approaches, while the research unit of analysis consists of Line managers and HR specialists. Purposeful sampling was used for the sampling process in this study while, the research sample consists of 373 Line managers and HR specialists. A questionnaire was used to collect data and PLS-SEM was used to evaluate the study. This study responds to two concerns raised in earlier e-HRM studies; first, to draw attention to the importance of the connection between technology and organisational features; and second, to indicate that the implementation of e-HRM leads to higher-quality HRM services. The main factors affecting the excellent quality of HRM services are the benefits of both traditional HRM and e-HRM. Repeated use of e-HRM and user guidance confirmed their mediating effect, while appropriation of e-HRM and acceptance of the e-HRM system demonstrated their moderating influence. This paper encourage line managers and HR specialists must ensure that e-HRM systems are user-friendly and understandable to the intended audience (e.g. Operational: e-payroll processes, Relational: e-recruitment and selection, Transformational: online talent management). This paper has made a significant contribution to knowledge and to the operationalization of e-HRM literature, which could help to develop theory, model, practice and research in areas of HRM quality of service.

The Impact of ChatGPT on Language Learners’ Motivation

As digital technology advances, its tools and applications are being used both inside and outside the classroom to increase student participation and motivation. One encouraging technology that has freshly seized acceptance is the adoption of artificial intelligence (AI) in language learning. One akin AI structure is ChatGPT. It is a language model matured by OpenAI that can engender human-like chat in return to user guidance. The goal of this study is to explore the effect of combining ChatGPT-generated dialogues into language teaching materials on the motivation of language learners. The participants group of this study were second-year 60 university students. The students' motivational strategies were measured using the 'Motivational Strategies for Learning Questionnaire' (Pintrinch &amp; De Groot, 1990). The outcomes of the present study showed that there were statistically powerful differences between majors on the motivation test subcategories (self-regulation, intrinsic values, and test anxiety). In other words, using ChatGPT technology as a learning material, has increased the motivation of the students.

Exploring the user guidance for more accurate building segmentation from high-resolution remote sensing images

In recent years, the computer vision domain has witnessed a surge of interest in interactive object segmentation, an area of study that seeks to expedite the annotation process for pixel-wise segmentation tasks through user guidance. Despite this growing focus, existing methods mainly focus on a single type of pre-annotation and neglect the quality of boundary prediction, which significantly influences subsequent manual adjustments to segmentation boundaries. To address these limitations, we introduce a novel end-to-end network to facilitate more precise building segmentation using diverse types of user guidance. In our proposed method, a centroid map is generated to provide foreground prior information crucial to the subsequent segmentation procedure, and the boundary correction module automatically refines the segmentation mask from existing segmentation networks. Extensive experiments on two popular building extraction datasets demonstrate that our method outperforms all existing approaches given various user guidance (bounding boxes, inside-outside points, or extreme points), achieving the IoU scores of over 95% on SpaceNet-Vegas dataset and over 93% on Inria-building dataset. The remarkable performance of our method further demonstrates its immense potential to alleviate the labor-intensive annotation process associated with remote sensing datasets. The code of our proposed method is available at https://github.com/StephenDHYang/UGBS-pytorch.