Evaluation Criteria Research Articles

Effects of Message Framing on Human Papillomavirus Vaccination: Systematic Review

Background With the advancement of cervical cancer elimination strategies, promoting human papillomavirus (HPV) vaccination is essential to achieving this goal. The issue of how to structure and develop message content to promote HPV vaccination is a debatable issue. Objective The efficacy of gain-loss framing in vaccination contexts is disputed. Our study aimed to elucidate the consequences of message framing on attitudes, intentions, and behavioral tendencies toward HPV vaccination, with the objective of refining message framing strategies and their elements. Methods This systematic review adhered strictly to the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analysis) guideline reporting standards to comprehensively retrieve, extract, and integrate data. We searched databases, including PubMed, Embase, Scopus, and Web of Science, for literature published from database construction to August 15, 2023. Literature screening, data extraction, and quality evaluation were performed by 2 researchers. Intervention studies published in English, conducted with populations with children eligible for HPV vaccination, and involving message framing were included. Attitudes, intentions, and behaviors served as outcome evaluation criteria. Results A total of 19 intervention studies were included. Gain-loss framing had no clear effect on vaccination attitudes nor intentions. Loss framing showed a weak advantage at improving HPV vaccination attitudes or intentions, but the evidence was not strong enough to draw definitive conclusions. The impact of gain-loss framing on HPV vaccination behaviors could not be determined due to the limited number of studies and the qualitative nature of the analysis. Conclusions Combining gain-loss framing with other message framing approaches may be an effective way to enhance the effect of message framing. More high-quality message framing content and exploring alternative moderator or mediator variables are required to support the conclusion. Trial Registration CRD42023451612; https://www.crd.york.ac.uk/PROSPERO/display_record.php?RecordID=451612

Can ChatGPT help patients understand radiopharmaceutical extravasations?

A previously published paper in the official journal of the Society of Nuclear Medicine and Molecular Imaging (SNMMI) concluded that the artificial intelligence chatbot ChatGPT may offer an adequate substitute for nuclear medicine staff informational counseling to patients in an investigated setting of 18F-FDG PET/CT. To ensure consistency with the previous paper, the author and a team of experts followed a similar methodology and evaluated whether ChatGPT could adequately offer a substitute for nuclear medicine staff informational counseling to patients regarding radiopharmaceutical extravasations. We asked ChatGPT fifteen questions regarding radiopharmaceutical extravasations. Each question or prompt was queried three times. Using the same evaluation criteria as the previously published paper, the ChatGPT responses were evaluated by two nuclear medicine trained physicians and one nuclear medicine physicist for appropriateness and helpfulness. These evaluators found ChatGPT responses to be either highly appropriate or quite appropriate in 100% of questions and very helpful or quite helpful in 93% of questions. The interobserver agreement among the evaluators, assessed using the Intraclass Correlation Coefficient (ICC), was found to be 0.72, indicating good overall agreement. The evaluators also rated the inconsistency across the three ChatGPT responses for each question and found irrelevant or minor inconsistencies in 87% of questions and some differences relevant to main content in the other 13% of the questions. One physician evaluated the quality of the references listed by ChatGPT as the source material it used in generating its responses. The reference check revealed no AI hallucinations. The evaluator concluded that ChatGPT used fully validated references (appropriate, identifiable, and accessible) to generate responses for eleven of the fifteen questions and used generally available medical and ethical guidelines to generate responses for four questions. Based on these results we concluded that ChatGPT may be a reliable resource for patients interested in radiopharmaceutical extravasations. However, these validated and verified ChatGPT responses differed significantly from official positions and public comments regarding radiopharmaceutical extravasations made by the SNMMI and nuclear medicine staff. Since patients are increasingly relying on the internet for information about their medical procedures, the differences need to be addressed.

Dupilumab for Chronic Hand Eczema: A Systematic Review and Meta-Analysis.

Chronic hand eczema (CHE) is a distressing and pervasive dermatologic condition, and effective treatment can be challenging. The novel IL-4/13 inhibitor dupilumab has shown clinical efficacy for atopic dermatitis (AD) and is being studied for CHE. PubMed database was searched for terms dupilumab and chronic hand eczema/dermatitis, including CHE subtypes (dyshidrotic, hyperkeratotic, allergic, contact, atopic, occupational, vesicular). Twenty-two study publications met inclusion and exclusion criteria for evaluation. Two multicenter prospective studies, one randomized controlled trial, two prospective observational studies, one retrospective study, nine case series, and seven case reports were included in the analysis with 374 total patients. Any partial response or complete resolution of CHE occurred in 80.3% of patients treated with dupilumab within 4-16 weeks of treatment. The patient response tended to increase throughout the duration of these studies for a significant percentage of patients, and for many it lasted beyond the study duration. Significant improvement in hand eczema severity scores and quality-of-life scores were noted. Efficacy was reported in over half of the cases of hyperkeratotic hand eczema; however, efficacy was decreased compared with relatively high consistent efficacy in other CHE subtypes such as dyshidrotic, vesicular, atopic, and contact CHE. Patients with current or previous AD had increased improvement scores compared to those without. The most common adverse effects were conjunctivitis (9.9%), herpes viral infection (1.4%), and facial erythema/dermatitis (1.3%). Dupilumab shows therapeutic efficacy in treating CHE. Further randomized controlled studies of dupilumab use in CHE subtypes are needed.

Peptide Receptor Radionuclide Therapy Improves Survival in Patients Who Progress After Resection of Gastroenteropancreatic Neuroendocrine Tumors.

Peptide receptor radionuclide therapy (PRRT) is an effective treatment for advanced gastroenteropancreatic (GEP) neuroendocrine tumors (NETs). We investigated a 2-decade experience with PRRT to determine whether PRRT confers a survival advantage to patients who progress after surgery versus other therapies. We identified patients from our clinic who had resection/cytoreduction of GEP-NETs, then disease progression by Response Evaluation Criteria in Solid Tumors (RECIST) 1.1. The Kaplan-Meier method assessed progression-free survival (PFS) and overall survival (OS), calculated from progression after surgery (no-PRRT group) or the start of PRRT. Cox regression with time-dependent covariates controlled for immortal time bias and other confounders. Overall, 237 patients progressed after surgery; 95 received PRRT and 142 did not. No differences existed in sex, T or Nstage, tumor grade/differentiation, primary site, or time to progression; 94% of PRRT patients had metastases at diagnosis versus 77% in the no-PRRT group. Median PFS was longer in the PRRT group versus the no-PRRT group (32.4 vs. 11.0 months, p<0.001), as was median OS (49.8 vs. 38.4 months; p=0.009). In subgroup analysis, the PRRT group had improved PFS in small bowel NETs and pancreatic NETs. Time-dependent covariate analysis revealed a lower risk of death associated with PRRT (hazard ratio 0.61, p=0.028) after adjusting for sex, age, Mstage, tumor grade, and primary site. Surgical resection and cytoreduction is an effective treatment for patients with GEP-NETs, but most patients with metastatic disease develop recurrent disease. Surgery followed by PRRT after progression conferred superior PFS and OS over no PRRT/other therapies, and is an effective strategy for managing patients with GEP-NETs.

Understanding beneficiary evaluative capacity within nonprofit organisations through an immanent perspective

PurposeThe purpose of this research was to seek a more refined understanding of the ways beneficiaries are evaluating nonprofit organisations (NPO), from the beneficiaries’ perspectives. Understanding evaluation from beneficiaries’ perspectives is not only important theoretically, but also for enabling evaluation processes to authentically contribute toward enhanced downward accountability.Design/methodology/approachTheorisation of immanent evaluation (Deleuze, 1998), the ontological view that there is no form imposed from outside or above but instead an articulation from within, was drawn upon to direct attention toward understanding beneficiaries’ inherent productive evaluative capacity and agency. This theorisation enabled a different way of observing and understanding beneficiary evaluation within a qualitative case study conducted in an Australian NPO. Data was sourced from interviews, observations and document analysis.FindingsFindings suggest beneficiaries largely viewed the NPO’s evaluation processes to be unsatisfactory toward meeting their needs in relation to meaningful engagement. However, beneficiaries’ evaluative capacity was noted to include their own evaluation criteria and evaluative expressions indicating the production of an evaluative account. Here beneficiaries’ evaluative expressions are representations of events of evaluation, initiated by them. Findings enable a more refined understanding of beneficiaries’ engagement in evaluation, moving beyond traditional considerations of participative evaluation, and illustrating beneficiaries’ agency and active role in the production of evaluation.Originality/valueThis research furthers understandings of downward accountability and participative evaluation by detailing how beneficiaries’ evaluative capacity is part of an NPO’s evaluative environment, and as such, conceives of an immanent theory of beneficiary evaluation. Findings highlight how evaluation, as a mechanism of downward accountability, functions from beneficiaries’ perspectives and the type of organisational environment capable of enabling and better supporting beneficiary engagement.

A Deep Learning Dependent Controller for Advanced Ultracapacitor SoC Concept to Increase Battery Life Span of Electric Vehicles

ABSTRACTOn a global scale, significant progress is being made in the field of battery technology for Electric Vehicle (EV) applications, driven by the need to combat carbon emissions and mitigate the effects of global warming. Accurately determining critical parameters, making sure battery storage system diagnosis, and functioning are correct are critical to the feasibility of EVs. However, insufficient supervision and safety measures for these storage systems may lead to serious problems like a thermal runaway, overcharging, overheating, cell imbalances, and fire hazards. To tackle these challenges, the presence of an efficient battery management system becomes paramount. By facilitating accurate monitoring, managing heat dissipation, regulating charging‐discharging procedures, guaranteeing battery safety, and offering protection measures, this system is essential to maximizing battery performance. The key intention of this innovative approach is to improve the longevity of EV batteries during extended periods of operation. By assessing vehicle velocity, remaining battery energy, and State of Charge (SoC), the proposed method effectively manages SoC in both the battery and ultracapacitor. This control is accomplished through a two‐stage convolutional neural network‐based system known as the Charge Sustain‐CNN Controller and the Charge Deplete‐CNN Controller. These controllers are fine‐tuned using the Fractional Latrans‐Hunt optimization (FLHO) algorithm to optimize the performance. The evaluation criteria encompass the battery and ultracapacitor's energy efficiency, as well as vehicle velocity. This novel approach significantly improves the energy storage system in EVs, leading to enhanced energy efficiency and prolonged battery life. Ultimately, experimental results validate the practicality and effectiveness of this developed method. Specifically, the proposed approach attained the Battery's SoC of 72.47%, 91.99%, and 82.88% for the different drive cycles including the FTP75, J1015, and UDDS, respectively.

Machine Learning Based Decision Support System for the Diagnosis of Breast Cancer

Breast cancer is among the most prevalent diseases encountered among women worldwide. Early diagnosis of breast cancer is crucial for the treatment of the disease. Detecting the disease at an early stage prevents deaths resulting from the condition. Recently, computer-aided systems have been developed to ensure early-stage diagnosis and accuracy of breast cancer. Computer-aided systems developed with machine learning approaches significantly contribute to the process of diagnosing breast cancer. The aim of this study is to propose a new classification system based on machine learning algorithms developed for the diagnosis of breast cancer. In this study, sub-data sets were created by reducing features, and data cleaning processes were applied. After these procedures, stages such as feature selection and feature extraction were applied. In this study, classification processes such as Ensemble, k- Nearest Neighbors (kNN), Support Vector Machines (SVMs), and Hybrid Artificial Intelligence were used in line with machine learning. With the obtained results, a Breast Cancer diagnosis algorithm was created. Performance evaluation criteria such as accuracy rate, specificity, sensitivity, kappa number and F-Measure were applied to the created algorithms. In the results obtained in this study, the highest accuracy rate was found to be 99.3% with the Ensemble method, the highest specificity rate was 98.7% with the Ensemble method, and the highest sensitivity rate was found to be 100% with many methods. In light of these results, it was observed that the machine learning algorithms used in this study, implemented in the Matlab environment, were effective. Consequently, it was proven that higher accuracy, specificity, and sensitivity rates can be found with different machine learning techniques. This also demonstrates that the study in our article is a reliable one in detecting diseased and healthy individuals in the diagnosis of breast cancer, showing that it is a more applicable and feasible study in the healthcare field.

Business Analytics in Customer Lifetime Value: An Overview Analysis

ABSTRACTIn customer‐oriented systems, customer lifetime value (CLV) has been of significant importance for academia and marketing practitioners, especially within the scope of analytical modeling. CLV is a critical approach to managing and organizing a company's profitability. With the vast availability of consumer data, business analytics (BA) tools and approaches, alongside CLV models, have been applied to gain deeper insights into customer behaviors and decision‐making processes. Despite the recognized importance of CLV, there is a noticeable gap in comprehensive analyses and reviews of BA techniques applied to CLV. This study aims to fill this gap by conducting a thorough survey of the state‐of‐the‐art investigations on CLV models integrated with BA approaches, thereby contributing to a research agenda in this field. The review methodology consists of three main steps: identification of relevant studies, creating a coding plan, and ensuring coding reliability. First, relevant studies were identified using predefined keywords. Next, a coding plan—one of the study's significant contributions—was developed to evaluate these studies comprehensively. Finally, the coding plan's reliability was tested by three experts before being applied to the selected studies. Additionally, specific evaluation criteria in the coding plan were implemented to introduce new insights. This study presents exciting and valuable results from various perspectives, providing a crucial reference for academic researchers and marketing practitioners interested in the intersection of BA and CLV.

A perceived community typology in older Korean Americans: implications for mental health.

The aims of this study were to identify a community typology in older Korean Americans and to examine how the typology is associated with feelings of loneliness and mental distress. We hypothesized that distinct community groups would be identified and that they would be differentially associated with mental health and background characteristics. Data were drawn from a survey with older Korean Americans aged 60 and older, collected during 2017-2018 in diverse locations (n = 2138). To identify a community typology, a series of latent profile analyses (LPA) were conducted using 15 community-related variables in three domains (neighbourhood characteristics, social cohesion, ethnic attachment). Based on model evaluation criteria, an LPA model with five community groups was identified as the best fit. The five groups were identified as "safe/integrated" (10%), "safe/distant" (10%), "moderate integration" (38%), "marginal" (31%), and "vulnerable" (11%). After examining descriptive characteristics of the identified groups, regression models of loneliness and mental distress were estimated. Using the safe/integrated group as reference, the marginal and vulnerable groups were consistently associated with elevated feelings of loneliness and mental distress. The results suggest the need to understand community profiles and their relationships with health/well-being among older immigrants.

Predicting Team Advancement in Major League Baseball Postseason Using Borda Count

The prediction of sports competition outcomes has long been a topic of interest in academia and among sports enthusiasts. This study focuses on Major League Baseball (MLB) as its research subject, encompassing the years 2020, 2021, and 2022. By employing a set of established evaluation criteria, comprising five pitching and five hitting indicators from previous literature, the regular-season performance of the 30 MLB teams across both leagues (National League and American League) over the three-year period was compiled. Subsequently, a data normalization technique combined with the Borda count concept was proposed to develop a model for forecasting team advancement in the postseason. The predictive accuracy of the model presented in this study for determining MLB postseason qualifiers from 2020 to 2022 fell within the range of 55.6% to 66.7%, akin to models utilizing extensive datasets. Notably, the proposed model is more comprehensible and user-friendly, offering ease of understanding and application for sports enthusiasts and facilitating its potential utilization and dissemination in the sporting community.

Efficacy, safety, and biomarker analysis of TACE combined with lenvatinib plus sintilimab in unresectable hepatocellular carcinoma: a real-world study

BackgroundThe integration of transarterial chemoembolization (TACE) with systemic therapy has demonstrated improved survival outcomes in patients with unresectable hepatocellular carcinoma (HCC). However, there is limited evidence evaluating the combination of TACE with the systemic regimen of anti-PD-1/L1 inhibitor plus lenvatinib. This study aims to assess the efficacy and safety of TACE combined with lenvatinib and sintilimab in unresectable HCC patients.MethodsUnresectable HCC patients who received TACE in combination with sintilimab plus Lenvatinib as first-line treatment from 1 January 2020 to 31 March 2023 were included for the analysis. Overall survival (OS), progression-free survival (PFS), objective response rate (ORR) and disease control rate (DCR) were evaluated by modified Response Evaluation Criteria in Solid Tumors criteria. Exploratory biomarker analysis was conducted.ResultsThe study included 70 patients with unresectable HCC, predominantly male and infected with Hepatitis B. The median follow-up duration for the whole cohort was 13.8 months (95% CI 11.08–16.7). The ORR was 61.4% (95% CI, 49.0%–72.8%) and the DCR was 68.6% (95%CI, 56.4%–79.2%). The median PFS was 13.2 months (95% CI 11.0-NA), with a corresponding 1-year PFS rate of 50.3% (95% CI 39.7%-65.5%). The median OS was not reached, and the 1-year OS rate was 89.3% (95% CI 81.4%–97.9%). The most common treatment-related adverse events (TRAEs) were fatigue 38.6% (27/70), hypertension 32.9% (23/70), and hand-foot syndrome 31.4% (22/70). Most TRAEs were mild-to-moderate and manageable. In addition, significant predictive value was found in alpha-fetoprotein levels (AFP), with patients showing a level of decrease post-treatment having better PFS.ConclusionThe combination regimen demonstrated promising efficacy in treating unresectable HCC, accompanied by manageable safety profiles. Furthermore, the results of this investigation suggest that AFP holds promise as predictive biomarkers for this treatment strategy.

Soluplus-Stabilized Nimodipine-Entrapped Spanlastic Formulations Prepared with Edge Activator (Tween20): Comparative Physicochemical Evaluation.

Nimodipine (ND) is a vasodilator drug that is used for acute subarachnoid hemorrhage. It has a predominant hydrophobic property, causing low solubility and low bioavailability. Spanlastics are elastic nanovesicular systems based on non-ionic surfactants and edge activators as major components. The goal of this work is to formulate ND as spanlastic nanovesicles to improve the drug's bioavailability. Spanlastic formulations containing ND were prepared by using the ethanol injection method. The composition of the ND formulation includes Span60 as a nonionic surfactant and Tween 20 as edge activators in different ratios. Stabilizers like Soluplus are used in some formulations and then compared with other formulations without that stabilizer. The evaluation study involved Vesicle Size (VS), PolyDispersity Index (PDI), and Entrapment Efficiency (%EE). Then, the optimized formula was subjected to an in vitro release study and zeta potential, additionally comparing the optimized formula with the formula without soluplus in the same concentration in Scanning Electron Microscopy (SEM), solubility study, Deformability Index (DI), and stability study. The results indicated a significant shift in some evaluation criteria and a non-significant change in other characterizations, including the difference in polymer ratio, sonication time, and the existence of a stabilizer. The best formula, F27, was found to have VS, PDI, %EE, and zeta potential of 125.7±0.29 nm, 0.4744±0.002, and 85.43±0.17% and -20.01 ± 0.89 mV, respectively. The photomicrographs of the prepared spanlastic revealed a more uniform and spherical spanlastic, indicating a greater capacity for continuous release. With the addition of Soluplus, the formula became more stable in one month and had a higher deformability index. A significant shift was observed in both VS and PDI. As the stabilizer concentration increases, VS and PDI will decrease. The non-significant shift was noted in the %EE with the presence of a stabilizer. Soluplus has the ability to spontaneously self-assemble into spherical particles. Additionally, PEG 6000, as a component of Soluplus's structure, has a tendency to form strong or tightly bound bilayers and prevent aggregation and formulation of large vesicles. This study explains the accessibility of the formulation of ND as spanlastic nanovesicles by using the ethanol injection method. This spanlastic formulation contains non-ionic surfactants and edge activators (Span 40 and Tween 20) in varying ratios. To get a stable formula, Soluplus is added to prevent the development of crystals and agglomeration.

A Study on Functional Requirements and Inspection Items for AI System Change Management and Model Improvement on the Web Platform

The rapid adoption of artificial intelligence (AI) on the web platform across multiple sectors has highlighted not only its inherent technical hurdles, such as unpredictability and lack of transparency, but also significant societal concerns. These include the misuse of AI technology, invasions of privacy, discrimination fueled by biased data, and infringements of copyright. Such challenges jeopardize the sustainable growth of AI and risk the erosion of societal trust, industry adoption and financial investment. This analysis explores the AI system’s lifecycle, emphasizing the essential continuous monitoring and the need for creating trustworthy AI technologies. It advocates for an ethically oriented development process to mitigate adverse effects and support sustainable progress. The dynamic and unpredictable nature of AI, compounded by variable data inputs and evolving distributions, requires consistent model updates and retraining to preserve the integrity of services. Addressing the ethical aspects, this paper outlines specific guidelines and evaluation criteria for AI development, proposing an adaptable feedback loop for model improvement. This method aims to detect and rectify performance declines through prompt retraining, thereby cultivating robust, ethically sound AI systems. Such systems are expected to maintain performance while ensuring user trust and adhering to data science and web technology standards. Ultimately, the study seeks to balance AI’s technological advancements with societal ethics and values, ensuring its role as a positive, reliable force across different industries. This balance is crucial for harmonizing innovation with the ethical use of data and science, thereby facilitating a future where AI contributes significantly and responsibly to societal well-being.

Artificial Intelligence as Research Methods in Urban Design

This paper delves into the role of artificial intelligence (AI) in urban design, focusing on its capability to manage complex urban systems via paradigm classification and comparative analysis. It methodically explores how AI improves design generation, optimizes morphology, and simulates real-world scenarios, thereby enhancing design quality and efficiency throughout the urban design process. Furthermore, the study contributes to the development of comprehensive AI evaluation criteria, integrating both theoretical and practical perspectives to critically understand the advantages and limitations of AI in urban design applications.

Construction of Sensory Evaluation System of Purple Sweet Potato Rice Steamed Sponge Cake Based on Fuzzy Mathematics

This paper presents the establishment of a sensory evaluation system for purple sweet potato rice steamed sponge cake (PSPRSSC) utilizing fuzzy mathematics. Initially, eight key sensory evaluation indices were identified through expert consultations. These indices were subsequently prioritized using fuzzy binary comparison, resulting in the following order: aroma, taste, crust color, elasticity, viscosity, chewiness, hardness, and brightness. The corresponding weight values assigned to each index were 23%, 18%, 16%, 13%, 12%, 8%, 6%, and 4%, respectively. Notably, aroma was found to have a more significant impact than visual attributes in the sensory evaluation of PSPRSSC. Based on this prioritization, comprehensive sensory evaluation criteria for PSPRSSC were formulated. Verification tests confirmed the efficacy of the proposed evaluation system. This study provides critical data and theoretical support for the sensory quality assessment of PSPRSSC, thereby facilitating its industrialization and enhancing its market viability.

Generative AI in education: ChatGPT-4 in evaluating students’ written responses

ABSTRACT The study highlights ChatGPT-4’s potential in educational settings for the evaluation of university students’ open-ended written examination responses. ChatGPT-4 evaluated 54 written responses, ranging from 24 to 256 words in English. It assessed each response using five criteria and assigned a grade on a six-point scale from fail to excellent, resulting in 3,240 evaluations. Verification-based chain-of-thought prompting with the RAG framework ensured ChatGPT-4’s accurate recall of responses and secure alignment in the university’s evaluation criteria. ChatGPT-4’s grading showed good consistency with the teacher’s grading. Mistakes in recalls and discrepancies between ChatGPT-4 and teacher assessments could be reduced. The results suggest a promising potential for using LLMs like ChatGPT-4 in academic written response evaluations.

Health Technology Assessment of Nailfold Capillaroscopy and Digital Retina Imaging in Diabetes Screening

AimThis study aims to identify the criteria for the evaluation of medical technologies in diabetes screening and then perform health technology assessment (HTA) for two technologies: digital retina imaging (DRI) and Nailfold Capillaroscopy (NFC). MethodsA multicriteria decision-making tool measuring attractiveness using a categorical-based evaluation technique (MACBETH) was used to calculate and compare alternatives. A focus group of ten decision-makers with more than ten years of experience in health technology was used for the decision-making. The Delphi Method was used to get a consensus about comparing criteria and alternatives to these criteria. The sampling method used in the study was the nonprobability purposive sampling method. ResultsThe study concluded that clinical efficacy (0.42) is the most important criterion for evaluating medical technologies, followed by cost-effectiveness (0.27). At the same time, ethical considerations were found to be less important (0.03). The sensitivity analysis concluded that the decision is sensitive to clinical efficacy and cost-effectiveness changes. The study found that none of the alternatives dominates others as an option for early detection of diabetes through vascular changes. LimitationsThis study primarily uses systemic literature review methods to identify criteria for evaluating alternatives. There may be additional criteria for evaluation. ConclusionThis study provides an approach for a multicriteria comparison of medical devices. The study findings are useful for public health professionals and health policymakers. Plain Language SummaryThis study assesses nailfold capillaroscopy (NFC) as a substitute for digital retina imaging (DRI) in diabetes screening using the MACBETH multicriteria decision-making tool. The research identifies five key evaluation criteria: clinical efficacy, cost-effectiveness, patient needs, user safety, and ethical considerations. A focus group of experienced health technology decision-makers was used to compare NFC and DRI. Results indicate that clinical efficacy and cost-effectiveness are the most critical factors, with NFC showing potential but not significantly outperforming DRI. Sensitivity analyses highlight the need for further research to validate NFC as an effective diabetes screening tool.

Image restoration through few-mode fiber using a new comprehensive attention model

Few-mode fibers (FMF) has unique advantages in single fiber imaging (SFI) in special applications. However, the limited number of transmission modes restricts the information carriers, hence the high-quality image restoration is a major challenge. A deep learning network with new comprehensive attention, called few-mode fiber Swin Transformer (FFST), is developed based on Swin Transformer V2 to realize successful image restoration with a FMF on handwriting letters and numbers. This network is proved outperforming nine widely used convolutional neural networks or Transformer-based models with four evaluation criteria. In addition, single-fiber-multi-location (SFML) experiment is conducted to investigate the effect of different view on imaging feasibility, the result of which shows that SFML can improve the restoration quality despite one object corresponds to multiple different speckle images, indicating that multi-mapping relationships has constructive effect in single FMF imaging. This paper provides a method for imaging in very narrow cavities.

Choreographing multi-degree of freedom behaviors in large-scale crowd simulations

This study introduces a novel framework for choreographing multi-degree of freedom (MDoF) behaviors in large-scale crowd simulations. The framework integrates multi-objective optimization with spatio-temporal ordering to effectively generate and control diverse MDoF crowd behavior states. We propose a set of evaluation criteria for assessing the aesthetic quality of crowd states and employ multi-objective optimization to produce crowd states that meet these criteria. Additionally, we introduce time offset functions and interpolation progress functions to perform complex and diversified behavior state interpolations. Furthermore, we designed a user-centric interaction module that allows for intuitive and flexible adjustments of crowd behavior states through sketching, spline curves, and other interactive means. Qualitative tests and quantitative experiments on the evaluation criteria demonstrate the effectiveness of this method in generating and controlling MDoF behaviors in crowds. Finally, case studies, including real-world applications in the Opening Ceremony of the 2022 Beijing Winter Olympics, validate the practicality and adaptability of this approach.

Research on Driver Emotion Monitoring and Regulation System in Intelligent Assisted Driving

Abstract. With the rapid development of smart connected vehicles, the influence, recognition and regulation of driver's emotions are crucial for intelligent cockpits with "emotional interaction". This review aims to provide a comprehensive analysis of driver emotion monitoring systems in intelligent assisted driving. By summarizing and analyzing related literature, the structure and application of emotion monitoring systems are introduced, their principles and evaluation criteria are discussed in depth, and the advantages and limitations of the three sub-systems, namely, detection, decision-making and regulation, are analyzed in detail. The results show that although emotion monitoring and modulation technologies perform well in enhancing driving safety and comfort, their practical applications still face challenges in terms of technology maturity, cost control, and privacy protection. In addition, the reliability and accuracy of current emotion monitoring systems in complex driving environments have yet to be improved. Future research should focus on interdisciplinary cooperation to integrate the latest achievements in psychology, computer science and engineering. At the same time, it is necessary to develop unified technical standards and evaluation guidelines to promote compatibility and interoperability between different systems and enhance research on privacy protection measures to ensure the security and confidentiality of user data. Through these efforts, the field of intelligent driving is expected to achieve comprehensive development and wide application, further enhancing driver safety and user experience.