Update Time Research Articles

COMPONENTS OF METABOLIC SYNDROME AS A RISK FACTOR FOR COVID-19 IN INDIA

Objective: Several patients with components of metabolic syndrome (MetS) like obesity, hypertension, diabetes mellitus and dyslipidemia is a risk factor for COVID-19 in India. Aims of our review to bring together current data about components of metabolic syndrome & COVID 19 infection. It specially looks the relation between components of MetS (Obesity, Diabetes mellitus, Hypertension and dyslipidemia) and COVID 19. At the time of writing the review is updated till time. Methods: Search of literature was done by using google search & PubMed engines for prepare review related to my article. Results: Patients with components of metabolic syndrome like obesity, diabetes, hypertension and dyslipidemia have increased evidence of incidence and severity of COVID-19. COVID-19 has effect on the pathophysiology of all these components of metabolic syndrome. The control of all these components of metabolic syndrome is important not only for patients who are infected with COVID-19, but also for those without the disease. Conclusions: It is commonly known that the existence of comorbidities such as increase in BMI, dyslipidemia, diabetes mellitus and hypertension is associated with additional severe course of COVID-19. So measurement of components of metabolic syndrome parameters is essential to better estimate the risk of COVID-19 and management and control of these components (BMI, blood glucose, blood pressure, lipid profile) of metabolic syndrome parameters will cause less possibility to occur COVID-19.

The AMA Insider: A Digital Reference for Timely Updates to AMA Style and Format

The AMA Insider: A Digital Reference for Timely Updates to AMA Style and Format

A Bibliometric Analysis on Visualising and Mapping Green Supply Chain Management

The emergent field of green supply chain management (GSCM) has recently piqued the interest of both academics and practitioners, owing to its crucial role in integrating sustainable environmental processes into the traditional supply chain. An essential research goal is to understand the overall structure of research and what has been completed in the area so far to build a strong research program to address forthcoming GSCM problems. The purpose of conducting a bibliometric analysis of GSCM in its current state is to gain insights into the existing body of knowledge, identify trends, and understand the research landscape. Several literature reviews concentrating on certain elements of GSCM have been published from 2002 to 2021 (data update time is April 10, 2021). Thus, this study used a bibliometric method to examine the present level of research on the GSCM. The Scopus database, one of the world-leading publishers-independent global citation databases, was used to search for documents related to GSCM. A bibliometric tool called Bibliometrix was executed to examine the data collected from the search, which generated 717 documents. This software was used to build maps based on scientific publication network data, revealing relationships between researchers, countries, and journals. This comprehensive mapping of the field serves to graphically depict the evolution of publications over time as well as identify current research interests. Finally, the findings of the bibliometric analysis point out a growing trend in green supply chain studies around the world, indicating that people are becoming more conscious of the need for environmental management and protection, and sustainable development.

Fully Dynamic Submodular Maximization over Matroids

Maximizing monotone submodular functions under a matroid constraint is a classic algorithmic problem with multiple applications in data mining and machine learning. We study this significant problem in the fully dynamic setting, where elements can be both inserted and deleted in real-time. Our main result is a randomized algorithm that maintains an efficient data structure with an \({\tilde{O}({k^{2}}{\varepsilon})}\) amortized update time (in the number of insertions and deletions) and yields a \({(4+O(\varepsilon))}\) -approximate solution with respect to the dynamic optimum, where \(k\) is the rank of the matroid.

Dynamic Matching with Better-than-2 Approximation in Polylogarithmic Update Time

We present dynamic algorithms with polylogarithmic update time for estimating the size of the maximum matching of a graph undergoing edge insertions and deletions with approximation ratio strictly better than 2 . Specifically, we obtain a \(1+\tfrac{1}{\sqrt {2}}+\epsilon \approx 1.707+\epsilon\) approximation in bipartite graphs and a \(1.973+\epsilon\) approximation in general graphs. We thus answer in the affirmative the value version of the major open question repeatedly asked in the dynamic graph algorithms literature. Our randomized algorithms’ approximation and worst-case update time bounds both hold w.h.p. against adaptive adversaries. Our algorithms are based on simulating new two-pass streaming matching algorithms in the dynamic setting. Our key new idea is to invoke the recent sublinear-time matching algorithm of Behnezhad (FOCS’21) in a white-box manner to efficiently simulate the second pass of our streaming algorithms, while bypassing the well-known vertex-update barrier.

A Certificate-Less Distributed Key Management Scheme for Space Networks

The specificity and complexity of space networks render the traditional key management mechanism no longer applicable. The certificate-less-based distributed spatial network key management scheme proposed in this paper combines the characteristics of space networks, solving the problems regarding the difficulty of implementing centralized key management in space networks and the excessive overhead required for maintaining public key certificates by constructing a distributed key generation center and establishing strategies such as private key updates, master key component updates, and session key negotiation. This method also avoids the key escrow problem inherent in existing identity-based key management schemes. This scheme provides the DPKG construction method for space networks; designs the update strategy for the DPKG node’s master key sharing, providing a specific update algorithm; introduces the batch private key update mechanism; and uses the mapping function to evenly distribute the node’s update requests throughout the update time period, avoiding the problem of overly concentrated update requests. After analysis and simulation verification, it was proven that the scheme can meet the necessary security requirements, offering good stability and scalability.

PSR J1227−6208 and its massive white dwarf companion: Pulsar emission analysis, timing update, and mass measurements

PSR J1227−6208 is a 34.53-ms recycled pulsar with a massive companion. This system has long been suspected to belong to the emerging class of massive recycled pulsar−ONeMg white dwarf systems such as PSR J2222−0137, PSR J1528−3146, and J1439−5501. Here, we present an updated emission and timing analysis with more than 11 years of combined Parkes and MeerKAT data, including 19 hours of high-frequency data from the newly installed MeerKAT S-band receivers. We measure a scattering timescale of 1.22 ms at 1 GHz with a flat scattering index of 3.33 < β < 3.62, and a mean flux density of 0.53 − 0.62 mJy at 1 GHz with a steep spectral index of 2.06 < α < 2.35. Around 15% of the emission is linearly and circularly polarised, but the polarisation angle does not follow the rotating vector model. Thanks to the sensitivity of MeerKAT, we successfully measure a rate of periastron advance of ω7 = 0.0171(11) deg yr−1, and a Shapiro delay with an orthometric amplitude of h3 = 3.6 ± 0.5 μs and an orthometric ratio of ς = 0.85 ± 0.05. The main source of uncertainty in our timing analysis is chromatic correlated dispersion measure noise, which we model as a power law in the Fourier space thanks to the large frequency coverage provided by the Parkes UWL receiver. Assuming general relativity and accounting for the measurements across all the implemented timing noise models, the total mass, companion mass, pulsar mass, and inclination angle are constrained at 2.3 < Mt/M⊙ < 3.2, 1.21 < Mc/M⊙ < 1.47, 1.16 < Mp/M⊙ < 1.69, and 77.5 < i/deg < 80.3. We also constrain the longitude of ascending node to either Ωa = 266 ± 78 deg or Ωa = 86 ± 78 deg. We argue against a neutron star nature of the companion based on the very low orbital eccentric of the system (e = 1.15 × 10−3), and instead classify the companion of PSR J1227−6208 as a rare, massive ONeMg white dwarf close to the Chandrasekhar limit.

A low-complexity error-feedback lattice-equalizer with phase tracking for underwater acoustic communications.

Recursive least squares (RLS)-based equalizers are hindered by their high complexity in underwater acoustic (UWA) communications. This article proposes an adaptive equalizer with a phase tracking method for the UWA communication, named the error-feedback lattice-equalizer (EFLE). First, we derive the algorithm for recursively solving the least squares problem from EFLE, introducing a lattice structure using time and order updates, thereby reducing the complexity to be linearly related to its length. The error-feedback mechanism used in computing reflection coefficients ensures the numerical stability of the algorithm. By focusing on the rapid tap rotation in time-varying channels, we design phase tracking in EFLE to further improve equalization performance. To verify the bit error rate (BER) performance of the proposed EFLE, we study the UWA communication system and conduct UWA simulations and at-sea experiments. Comparisons include linear complexity equalizers such as least mean square (LMS), leaky LMS, least mean mixed-norm, and ϵ-normalized LMS equalizers, and quadratic complexity RLS equalizers. At-sea experiment results show that the BER performance of EFLE significantly outperforms its counterparts.

Chatbot for Promoting Best Crop Management Practices to Rice Farmers in Odisha, India

The increasing penetration of mobile phones has made it easier to reach remote farmers and bridge the knowledge gap between them and researchers/extension workers and agricultural stakeholders. Farmers need concise, context-specific, information to address the challenges which arise due to lack of near-real time updates. Scaling up innovative agricultural technologies is essential to ensure these advancements reach the farmers for their benefit. Chatbots represent an informative tool for delivering the latest agricultural developments to farmers in a user-friendly and interactive format. Chatbots can deliver information on best agronomic practices, weather forecast, market information thereby improving the decision-making capacity of farmers. This paper discusses the development of a rule-based chatbot under a pilot-study and presents findings from a user study conducted with 102 farmers in Odisha, India in 2021. The content topics were decided using a participatory approach wherein farmers selected the topic of interests and information required. The content was developed using the digital tools Rice Crop Manager (RCM), Seed Cast and Rice Knowledge Bank (RKB), developed and deployed by International Rice Research Institute (IRRI). The chatbot is hosted on Facebook messenger platform and needs the internet support to operate. Farmers having this app can use the chatbot without any additional expense. The results indicate that farmers acknowledged and appreciated interactive features of chatbot and the content but expressed a desire for more comprehensive information on insect-pest management and other crop management practices. The findings highlight the potential of chatbot as a complementary channel to disseminate agro advisories, complementing the existing agricultural extension services. The study has its limitations in terms of small sample size which may or may not represent the variability of the entire population of the area.

Optimizing workflow for OncotypeDx result turnaround time from surgery to report at safety net hospital.

317 Background: OncotypeDx test has been widely used for breast cancers patients with early-stage (I-IIIa), hormone-receptor positive, HER2-negative disease. It predicts the benefit of adjuvant chemotherapy in addition to hormone therapy. Delivering OncotypeDx results in a timely manner is important to inform treatment decisions. As a quality improvement project, we implemented a strategy to reduce the Turn Around Time (TAT) from breast surgery to OncotypeDx report at Bellevue Cancer Center, the tertiary referral center of the NYC Health and Hospitals System. Methods: The primary measure was TAT from surgery to receiving OncotoypeDx result in electronic medical record (EMR). We compared TAT before and after implementation of the strategy. The historical control was from 5/2021 through 3/2022, while the group after strategy implementation was from 6/2023 to 1/2024. The strategy revolved around streamlining the identification of eligible patients by collaborating with the breast surgery team, creating and utilizing a smart phrase in the EMR. It also involved ordering OncotypeDx by closely communicating with the pathology department, obtaining insurance approval by working with the OncotypeDx vendor, and tracking OncotypeDx results. The above process was done by a breast surgery provider that offered real time updates in the EMR. Results: The patient groups were similar both before and after implementing the strategy, including patient number (26 and 27 respectively) and patient mean age (61 and 59). The majority of both groups were minority patients (77% and 81%). We found that a higher percentage of patients were from Medically Underserved Areas and Populations (MUA/P) in the group after strategy implementation (35% and 56%). Our strategy reduced the average TAT from 56 days to 30 days. Conclusions: We developed a strategy to optimize the OncotypeDx workflow in a large safety net health system despite an increase in patients from MUA/P. Initiating ordering of OncotypeDx by breast surgery along with communication with pathology and the vendor significantly reduced TAT. EMR integration with OncotypeDx vendor would be the next step to further improve the process. Before Strategy Implementation (n=26) After Strategy Implementation (n=27) Patient Mean Age 61 59 Race (%) White 27 18.5 Hispanic 39 44.4 Black 7 25.9 Asian 23 11.1 Other 4 0 Ethnicity (%) Hispanic 39 44.4 Non-Hispanic 61 55.6 Underserved Area and Population (%) 35 56 Health Professional Shortage Area (%) 50 59 Non-English Speaking (%) 50 40.7

VLFSE: Enhancing visual tracking through visual language fusion and state update evaluator

Recently, visual tracking algorithms have achieved impressive results by combining dynamic templates. However, the instability of visual images and the incorrect timing of template updates lead to decreased tracking accuracy and stability in intricate scenarios. To address these issues, we propose a visual tracking algorithm through visual language fusion and a state update evaluator (VLFSE). Specifically, our approach introduces a multimodal attention mechanism that uses self-attention to mine and integrate information from diverse sources effectively. This mechanism ensures a richer, context-aware representation of the target, enabling more accurate tracking even in complex scenes. Moreover, we recognize the critical need for precise template updates to maintain tracking accuracy over time. To this end, we develop a state update evaluator, a component trained online to assess the necessity and timing of template updates accurately. This evaluator acts as a safeguard, preventing erroneous updates and ensuring the tracker adapts optimally to changes in the target’s appearance. The experimental results on challenging visual language tracking datasets demonstrate our tracker’s superior performance, showcasing its adaptability and accuracy in complex tracking scenarios.

Community Specialist Team for Older Adults - Who Do You Think We Are? Co-Design Of A Patient Information Leaflet

Abstract Background Navigating care pathways can be challenging and confusing for patients and caregivers. With the emergence of several new clinical specialist teams, we identified the need for accurate, clear and relevant information, provided as a printed leaflet and available at referral stage for both older adults and referring practitioners. Methods A draft Patient Information Leaflet (PIL) was developed with contributions from all members of the Community Specialist Team for Older Adults (CSTOA) explaining the purpose of the service. A review of referral sources was undertaken to identify those key stakeholders from whom feedback should be sought to ensure content validity. The draft PIL and a feedback questionnaire were distributed to patients and their caregivers who have had intervention from the CSTOA in addition to General Practitioners and Community Health Network colleagues. Results There were 27/35 responses to the questionnaire. Data was collated and analysed. All respondents reported that from reading the PIL, they understood clearly what the service provides, and all agreed that the leaflet accurately describes the service. Details such as what to expect from intervention of the team and advice to bring a family member to the initial appointment were mentioned as particularly useful content. Supplementary feedback commended the co-location and accessibility of the service compared to a hospital setting. It was suggested that other services should be included in the CSTOA. Based on survey responses, minor edits were made to the PIL to meet stakeholder satisfaction. Conclusion Feedback from this survey helped endorse the provision of a printed PIL to be provided at referral stage. Additional commentary gathered, re-enforces the need for continuous interaction and information sharing with service stakeholders. Next steps include making this PIL available electronically to allow for real time updates and translating to other languages to ensure inclusivity.

Bus arrival and departure time updates in the Greater Sydney Area

Public Transport Authorities generate large quantities of data as part of their daily operations, including vehicle positions, arrival times, and dynamic routing options. This information is essential feedback into the system for planning routes and timetables, modelling passenger demand growth and evaluating operational performance. The General Transit Feed Specification (GTFS) is a data format that allows public transport data to be consumed by a wide variety of software applications. There are barriers to widespread consumption of GTFS data related to location-specific data extensions, non-human-readable formats and error cleaning. This paper describes a flexible dataset of actual bus arrivals and departure times created with a pipeline for GTFS realtime feeds designed to address these challenges. The paper describes the pipeline, verifies the quality of the data and presents an output of 25 months of actual bus arrival and departure times for Sydney, Australia. We conclude by discussing relevance to researchers and practitioners of the pipeline outputs in general and the sample data specifically.

Assessment of Eye Care Apps for Children and Adolescents Based on the Mobile App Rating Scale: Content Analysis and Quality Assessment.

In China, the current situation of myopia among children and adolescents is very serious. Prevention and control of myopia are inhibited by the lack of medical resources and the low awareness about eye care. Nevertheless, mobile apps provide an effective means to solve these problems. Since the health app market in China is still immature, it has become particularly important to conduct a study to assess the quality of eye-care apps to facilitate the development of better eye-care service strategies. This study aimed to evaluate the quality, functionality, medical evidence, and professional background of eye-care apps targeting children and adolescents in the Chinese app stores. A systematic search on iOS and Android app stores was performed to identify eye-care apps for children and adolescents. The general characteristics, development context, and functional features of the apps were described. Quality assessment of the apps was completed by 2 independent researchers using the Mobile App Rating Scale. This study included 29 apps, of which 17 (59%) were developed by commercial organizations and 12 (41%) had a design with relevant scientific basis. The main built-in functions of these apps include self-testing (18/29, 62%), eye exercises (16/29, 55%), and eye-care education (16/29, 55%). The mean overall quality of eye-care apps was 3.49 (SD 0.33), with a score ranging from 2.89 to 4.39. The overall Mobile App Rating Scale score exhibited a significant positive correlation with the subscale scores (r=0.81-0.91; P<.001). In addition, although most apps provided basic eye-care features, there are some deficiencies. For example, only a few apps were developed with the participation of medical organizations or professional ophthalmologists, and most of the apps were updated infrequently, failing to provide the latest eye-care information and technology in a timely manner. In general, the quality of eye-care apps for children and teenagers in Chinese app stores is good. These apps fulfill users' needs for eye-care services to a certain extent, but they still suffer from insufficient medical background, low user engagement, and untimely updates. In order to further improve the effectiveness of eye-care apps, cooperation with medical institutions and professional ophthalmologists should be strengthened to enhance the scientific and authoritative nature of the apps. At the same time, interactive features and regular updates should be added to enhance user participation and the continuity of the apps. This study provides a reference for future development or improvement of eye-care apps, which can help promote myopia prevention and control.

Machine-learning-aided method for optimizing beam selection and update period in 5G networks and beyond

Finding the optimal beam pair and update time in 5G systems operating at mmWave frequencies is time-intensive and resource-demanding. This intricate procedure calls for the proposal of more intelligent approaches. Therefore, this work proposes a machine learning-based method for optimizing beam pair selection and its update time. The method is structured around three main modules: spatial characterization of beam pair service areas, training of a machine learning model using collected beam pair data, and an algorithm that uses the decision function of the trained model to compute the optimal update time for beam pairs based on the spatial position and velocity of user equipment. When the machine learning model is deployed in a network with a single gNB equipped with a 8×8\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$8\ imes 8$$\\end{document} UPA and one UE equipped with a 1×2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$1\ imes 2$$\\end{document} UPA in an mmWave scenario simulated in NS3, improvements in SINR and throughput up to 407%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$407\\%$$\\end{document}, were observed. Improvements are gathered because of a reduction of 85.7%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$85.7\\%$$\\end{document} in beam pair selections because of an increase of approximately 1543%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$1543\\%$$\\end{document} in the effective time between successive beam pair searches. This method could offer real-time optimization of the beam pair procedures in 5G networks and beyond.

Towards sustainable exploitation of European pilchard (Sardina pilchardus) stocks off West African waters

European pilchard (Sardina pilchardus), herein referred to as “sardine” or “European sardine”, is the most productive and economically important small pelagic fish in the Northwest African waters. Thus far, very little is known about its stock status in the West African region despite the rapid decadal increase in its catch production. The present study provides avenues vital to improving guidance for sustainable species management. In this region, sardine is divided administratively into three stocks (northern: stock N, central: stock A + B, and southern: stock C). Thus, the present study used two Surplus Production Models (SPM) to evaluate sardine's stock status. Sardine's catch and scientific survey biomass data obtained from FAO-CECAF's (Fishery Committee for the Eastern Central Atlantic (CECAF)) latest reports were used in this analysis to fit the SPMs and investigate the effectiveness of the available catch-per-unit-effort (CPUE) trends in setting the total allowable catch (TAC) for sustaining the different sardine stocks in the region. Results from the SPMs indicate that the sardine stock in the north is fished at optimum levels. Meanwhile, the SPMs output based on the scientific survey biomass indices indicated a healthy state for the central and southern sardine stocks. Similarly, the fishery CPUE indices for the central and southern stocks showed similar biomass trends compared to the survey biomass index, indicating healthy stock status. Therefore, in cases where survey biomass index data is unavailable to run stock assessments, this study suggests that sardine fishery CPUEs could be used in SPMs to monitor biomass trends over time and may also provide stock status proxies. Additionally, with rapid climate and other environmental changes, sardine stocks must be regularly monitored with consistent and comparable scientific methodologies and updated fisheries time series data.

Class-Incremental Learning Method With Fast Update and High Retainability Based on Broad Learning System.

Machine learning aims to generate a predictive model from a training dataset of a fixed number of known classes. However, many real-world applications (such as health monitoring and elderly care) are data streams in which new data arrive continually in a short time. Such new data may even belong to previously unknown classes. Hence, class-incremental learning (CIL) is necessary, which incrementally and rapidly updates an existing model with the data of new classes while retaining the existing knowledge of old classes. However, most current CIL methods are designed based on deep models that require a computationally expensive training and update process. In addition, deep learning based CIL (DCIL) methods typically employ stochastic gradient descent (SGD) as an optimizer that forgets the old knowledge to a certain extent. In this article, a broad learning system-based CIL (BLS-CIL) method with fast update and high retainability of old class knowledge is proposed. Traditional BLS is a fast and effective shallow neural network, but it does not work well on CIL tasks. However, our proposed BLS-CIL can overcome these issues and provide the following: 1) high accuracy due to our novel class-correlation loss function that considers the correlations between old and new classes; 2) significantly short training/update time due to the newly derived closed-form solution for our class-correlation loss without iterative optimization; and 3) high retainability of old class knowledge due to our newly derived recursive update rule for CIL (RULL) that does not replay the exemplars of all old classes, as contrasted to the exemplars-replaying methods with the SGD optimizer. The proposed BLS-CIL has been evaluated over 12 real-world datasets, including seven tabular/numerical datasets and six image datasets, and the compared methods include one shallow network and seven classical or state-of-the-art DCIL methods. Experimental results show that our BIL-CIL can significantly improve the classification performance over a shallow network by a large margin (8.80%-48.42%). It also achieves comparable or even higher accuracy than DCIL methods, but greatly reduces the training time from hours to minutes and the update time from minutes to seconds.

An optimal midcourse guidance method for dual pulse air-to-air missiles using linear Gauss pseudospectral model predictive control method

This paper proposes an optimal midcourse guidance method for dual pulse air-to-air missiles, which is based on the framework of the linear Gauss pseudospectral model predictive control method. Firstly, a multistage optimal control problem with unspecified terminal time is formulated. Secondly, the control and terminal time update formulas are derived analytically. In contrast to previous work, the derivation process fully considers the Hamiltonian function corresponding to the unspecified terminal time, which is coupled with control, state, and costate. On the assumption of small perturbation, a special algebraic equation is provided to represent the equivalent optimal condition for the terminal time. Also, using Gauss pseudospectral collocation, error propagation dynamical equations involving the first-order correction term of the terminal time are transformed into a set of algebraic equations. Furthermore, analytical modification formulas can be derived by associating those equations and optimal conditions to eliminate terminal error and approach nonlinear optimal control. Even with their mathematical complexity, these formulas produce more accurate control and terminal time corrections and remove reliance on task-related parameters. Finally, several numerical simulations, comparisons with typical methods, and Monte Carlo simulations have been done to verify its optimality, high convergence rate, great stability and robustness.

Dynamic Euclidean bottleneck matching

A fundamental question in computational geometry is for a set of input points in the Euclidean space, that is subject to discrete changes (insertion/deletion of points at each time step), whether it is possible to maintain an exact/approximate minimum weight perfect matching and/or bottleneck matching (a perfect matching that minimizes the length of the longest matched edge), in sublinear update time. In this work, we answer this question in the affirmative for points on a real line and for points in the plane with a bounded geometric spread.For a set P of n points on a line, we show that there exists a dynamic algorithm that maintains an exact bottleneck matching of P and supports insertion and deletion in O(log⁡n) time. Moreover, we show that a modified version of this algorithm maintains an exact minimum-weight perfect matching with O(log⁡n) update (insertion and deletion) time. Next, for a set P of n points in the plane, we show that a (62)-factor approximate bottleneck matching of Pk, at each time step k, can be maintained in O(log⁡Δ) amortized time per insertion and O(log⁡Δ+|Pk|) amortized time per deletion, where Δ is the geometric spread of P (the ratio between the diameter of P and the distance between the closest pair of points in P).

Should I See You Again Soon? Multispecialty Assessment of Impact and Burden of Preoperative History and Physical Update Visits.

Federal regulations require a history and physical (H&P) update performed 30 days or less before a planned procedure. We evaluated the use and burdens of H&P update visits by determining impact on operative management, suitability for telehealth, and visit time and travel burden. We identified H&P update visits performed in our health system during 2019 for 8 surgical specialties. As available, up to 50 visits per specialty were randomly selected. Primary outcomes were interval changes in history, examination, or operative plan between the initial and updated H&P notes, and visit suitability for telehealth, as determined by 2 independent physician reviewers. Clinic time was captured, and round-trip driving time and distance between patients' home and clinic ZIP codes were estimated. We identified 8,683 visits and 362 were randomly selected for review. Documented changes were most commonly identified in histories (60.8%), but rarely in physical examinations (11.9%) and operative plans (11.6%). Of 362 visits, 359 (99.2%) visits were considered suitable for telehealth. Median clinic time was 52 minutes (interquartile range 33.8 to 78), driving time was 55.6 minutes (interquartile range 35.5 to 85.5), and driving distance was 20.2 miles (interquartile range 8.5 to 38.4). At the health system level, patients spent an estimated aggregate 7,000 hours (including 4,046 hours of waiting room and travel time) and drove 142,273 miles to attend in-person H&P update visits in 2019. Given their minimal impact on operative management, regulatory requirements for in-person H&P updates should be reconsidered. Flexibility in update timing and modality might help defray the substantial burdens these visits impose on patients.