Comparing Pricing Mechanisms for Managed Lanes

Two common means of pricing managed lanes (MLs) are to vary tolls based on time of day or to vary them dynamically based on real-time congestion. It is not clear which of the two tolling options is more effective in regulating ML usage. In this research, large datasets on toll prices, vehicle travel speeds, and traffic volumes were used to assess the effects of the two different congestion pricing strategies on traffic conditions on six MLs around the United States. The MLs included two variably priced: SR-91 and I-25; and four that were dynamically priced: I-35W, I-394, I-35E, and MoPac. The research used nine different performance measures to examine the ability of the toll to regulate traffic on the MLs. These included travel time savings, variability benefit, planning time index benefit, mobility benefit, buffer time index benefit, the ability of the toll to impact congestion, speed threshold achievement, speed graphs, and scoring index. These performance measures included several unique measures developed as part of this research and proved useful in measuring how well the ML toll was able to regulate traffic flow and keep the MLs operating smoothly. Using these nine performance measures, the impact of the two pricing approaches on traffic conditions was evaluated. Overall, both pricing measures were found to keep traffic flowing on MLs, and neither pricing method was clearly superior. Although there was no clear winner, this does mean that both pricing mechanisms can work to keep MLs flowing and thus are viable options for ML operators. One item for future research would be to apply these performance measures to additional ML datasets to see if one method does perform significantly better given a larger set of MLs to investigate. These performance measures are a key contribution of this research and provide excellent benchmarking for the effectiveness of tolling on regulating ML traffic flow

Aspects of chiral transition in a Hadron Resonance Gas model

We study the chiral condensate for 2 + 1 flavor QCD with physical quarks within a non-interacting Hadron Resonance Gas (HRG) model. By including the latest information on the mass variation of the hadrons concerning the light quark mass, from lattice QCD and chiral perturbation theory, we show that it is possible to quite accurately account for the chiral crossover transition even within a conventional HRG model. We have calculated a pseudocritical temperature Tc=161.2±1.6 MeV and the curvature of crossover curve κ2=0.0203(7). These are in very good agreement with the latest continuum extrapolated results obtained from lattice QCD studies. We also discuss the limitations of extending such calculations toward the chiral limit. Furthermore, we study the effects of non-resonant hadron interactions within the HRG model and its consequences for the chiral transition in the regime of dense baryonic matter where lattice QCD results are not currently available

Empirical Optimal Transport between Conditional Distributions

Given samples from two joint distributions, we consider the problem of Optimal Transportation (OT) between the corresponding distributions conditioned on a common variable. The objective of this work is to estimate the associated transport cost (Wasserstein distance) as well as the transport plan between the conditionals as a function of the conditioned value. Since matching conditional distributions is at the core of supervised training of discriminative models and (implicit) conditional-generative models, OT between conditionals has the potential to be employed in diverse machine learning applications. However, since the conditionals involved in OT are implicitly specified via the joint samples, it is challenging to formulate this problem, especially when (i) the variable conditioned on is continuous and (ii) the marginal of this variable in the two distributions is different. We overcome these challenges by employing a specific kernel MMD (Maximum Mean Discrepancy) based regularizer that ensures the marginals of our conditional transport plan are close to the conditionals specified via the given joint samples. Under mild conditions, we prove that our estimator for this regularized transport cost is statistically consistent and derive finite-sample bounds on the estimation error. Application-specific details for parameterizing our conditional transport plan are also presented. Furthermore, we empirically evaluate our methodology on benchmark datasets in applications like classification, prompt learning for few-shot classification, and conditional-generation in the context of predicting cell responses to cancer treatment

Physical activity, time spent outdoors, and near work in relation to myopia prevalence, incidence, and progression:An overview of systematic reviews and meta-analyses

Myopia has reached epidemic levels in recent years. Stopping the development and progression of myopia is critical, as high myopia is a major cause of blindness worldwide. This overview aims at finding the association of time spent outdoors (TSO), near work (NW), and physical activity (PA) with the incidence, prevalence, and progression of myopia in children. Literature search was conducted in PubMed, Scopus, Cumulative Index to Nursing and Allied Health Literature, Cochrane Database of Systematic Reviews, ProQuest, and Web of Science databases. Systematic reviews (SR) and meta-analyses (MA) on the TSO, NW, and PA in relation to myopia were reviewed. Methodological nature of qualified studies were evaluated utilizing the Risk of Bias in Systematic Review tool. We identified four SRs out of which three had MA, which included 62 unique studies, involving >1,00,000 children. This overview found a protective trend toward TSO with a pooled odds ratio (OR) of 0.982 (95% confidence interval (CI) 0.979-0.985, I 2 = 93.5%, P < 0.001) per extra hour of TSO every week. A pooled OR 1.14 (95% CI 1.08-1.20) suggested NW to be related to risk of myopia. However, studies associating myopia with NW activities are not necessarily a causality as the effect of myopia might force children to indoor confinement with more NW and less TSO. PA presented no effect on myopia. Though the strength of evidence is less because of high heterogeneity and lack of clinical trials with clear definition, increased TSO and reduced NW are protective against myopia development among nonmyopes

Light and myopia: from epidemiological studies to neurobiological mechanisms

Myopia is far beyond its inconvenience and represents a true, highly prevalent, sight-threatening ocular condition, especially in Asia. Without adequate interventions, the current epidemic of myopia is projected to affect 50% of the world population by 2050, becoming the leading cause of irreversible blindness. Although blurred vision, the predominant symptom of myopia, can be improved by contact lenses, glasses, or refractive surgery, corrected myopia, particularly high myopia, still carries the risk of secondary blinding complications such as glaucoma, myopic maculopathy, and retinal detachment, prompting the need for prevention. Epidemiological studies have reported an association between outdoor time and myopia prevention in children. The protective effect of time spent outdoors could be due to the unique characteristics (intensity, spectral distribution, temporal pattern, etc.) of sunlight that is lacking in artificial lighting. Concomitantly, studies in animal models have highlighted the efficacy of light and its components in delaying or even stopping the development of myopia and endeavoured to elucidate possible mechanisms involved in this process. In this narrative review, we (1) summarize the current knowledge concerning light modulation of ocular growth and refractive error development based on studies in human and animal models, (2) summarize potential neurobiological mechanisms involved in the effects of light on ocular growth and emmetropization and (3) highlight a potential pathway for the translational development of noninvasive light-therapy strategies for myopia prevention in children.info:eu-repo/semantics/publishedVersio

Utility of artificial intelligence-based large language models in ophthalmic care

Purpose: With the introduction of ChatGPT, artificial intelligence (AI)-based large language models (LLMs) are rapidly becoming popular within the scientific community. They use natural language processing to generate human-like responses to queries. However, the application of LLMs and comparison of the abilities among different LLMs with their human counterparts in ophthalmic care remain under-reported. Recent Findings: Hitherto, studies in eye care have demonstrated the utility of ChatGPT in generating patient information, clinical diagnosis and passing ophthalmology question-based examinations, among others. LLMs' performance (median accuracy, %) is influenced by factors such as the iteration, prompts utilised and the domain. Human expert (86%) demonstrated the highest proficiency in disease diagnosis, while ChatGPT-4 outperformed others in ophthalmology examinations (75.9%), symptom triaging (98%) and providing information and answering questions (84.6%). LLMs exhibited superior performance in general ophthalmology but reduced accuracy in ophthalmic subspecialties. Although AI-based LLMs like ChatGPT are deemed more efficient than their human counterparts, these AIs are constrained by their nonspecific and outdated training, no access to current knowledge, generation of plausible-sounding ‘fake’ responses or hallucinations, inability to process images, lack of critical literature analysis and ethical and copyright issues. A comprehensive evaluation of recently published studies is crucial to deepen understanding of LLMs and the potential of these AI-based LLMs. Summary: Ophthalmic care professionals should undertake a conservative approach when using AI, as human judgement remains essential for clinical decision-making and monitoring the accuracy of information. This review identified the ophthalmic applications and potential usages which need further exploration. With the advancement of LLMs, setting standards for benchmarking and promoting best practices is crucial. Potential clinical deployment requires the evaluation of these LLMs to move away from artificial settings, delve into clinical trials and determine their usefulness in the real world

Assessing the utility of ChatGPT as an artificial intelligence‐based large language model for information to answer questions on myopia

Purpose ChatGPT is an artificial intelligence language model, which uses natural language processing to simulate human conversation. It has seen a wide range of applications including healthcare education, research and clinical practice. This study evaluated the accuracy of ChatGPT in providing accurate and quality information to answer questions on myopia. Methods A series of 11 questions (nine categories of general summary, cause, symptom, onset, prevention, complication, natural history, treatment and prognosis) were generated for this cross-sectional study. Each question was entered five times into fresh ChatGPT sessions (free from influence of prior questions). The responses were evaluated by a five-member team of optometry teaching and research staff. The evaluators individually rated the accuracy and quality of responses on a Likert scale, where a higher score indicated greater quality of information (1: very poor; 2: poor; 3: acceptable; 4: good; 5: very good). Median scores for each question were estimated and compared between evaluators. Agreement between the five evaluators and the reliability statistics of the questions were estimated. Results Of the 11 questions on myopia, ChatGPT provided good quality information (median scores: 4.0) for 10 questions and acceptable responses (median scores: 3.0) for one question. Out of 275 responses in total, 66 (24%) were rated very good, 134 (49%) were rated good, whereas 60 (22%) were rated acceptable, 10 (3.6%) were rated poor and 5 (1.8%) were rated very poor. Cronbach's α of 0.807 indicated good level of agreement between test items. Evaluators' ratings demonstrated ‘slight agreement’ (Fleiss's κ, 0.005) with a significant difference in scoring among the evaluators (Kruskal–Wallis test, p < 0.001). Conclusion Overall, ChatGPT generated good quality information to answer questions on myopia. Although ChatGPT shows great potential in rapidly providing information on myopia, the presence of inaccurate responses demonstrates that further evaluation and awareness concerning its limitations are crucial to avoid potential misinterpretation