Common Approach Research Articles

PT7 Data Standardization in Brazil: An OMOP Common Data Model Approach in a DATASUS Cohort

PT7 Data Standardization in Brazil: An OMOP Common Data Model Approach in a DATASUS Cohort

Weighting climate models for hydrological projections: effects on contrasting hydroclimatic regions

Weighting climate models has recently become a more accepted approach. However, it remains a topic of ongoing discussion, especially for analyses needed at regional scales, such as hydrological assessments. Various studies have evaluated the weighting approaches for climate simulations. Yet, few case studies have assessed the impacts of weighting climate models on streamflow projections. Additionally, the methodological and location limitations of previous studies make it difficult to extrapolate their conclusions over regions with contrasting hydroclimatic regimes, highlighting the need for further studies. Thus, this study evaluates the effects of different climate model’s weighting approaches on hydrological projections over hydrologically diverse basins. An ensemble of 24 global climate model (GCM) simulations coupled with a lumped hydrological model is used over 20 North American basins to generate 24 GCM-driven streamflow projections. Six unequal-weighting approaches, comprising temperature-, precipitation-, and streamflow-based criteria, were evaluated using an out-of-sample approach during the 1976–2005 reference period. Moreover, the unequal-weighting approaches were compared against the equal-weighting approach over the 1976–2005, 2041–2070, and 2070–2099 periods. The out-of-sample assessment showed that unequally weighted ensembles can improve the mean hydrograph representation under historical conditions compared to the common equal-weighting approach. In addition, results revealed that unequally weighting climate models not only impacted the magnitude and climate change signal, but also reduced the model response uncertainty spread of hydrological projections, particularly over rain-dominated basins. These results underline the need to further evaluate the adequacy of equally weighting climate models, especially for variables with generally larger uncertainty at regional scale.

Investigation of the Working Features of Concrete Structures Manufactured Using Technologies of Additive Building Manufacturing

The most common approach to the application of additive manufacturing technology today, which provides for printing the contour of buildings and structures with the creation of non-removable concrete formwork for the construction of load-bearing and enclosing structures. The features of the operation of a non-removable formwork under the action of lateral pressure of a concrete mixture are investigated. The results of the experimental research stage implemented on the basis of the SMITH Research Institute of the Moscow State University of Civil Engineering are presented, in which the strength characteristics of samples selected from single-layer concrete structures made using additive manufacturing technology, as well as the bearing capacity of fragments of non-removable formwork of rectangular and closed cylindrical shape under the influence of simulated pressure of a concrete mixture were studied. In the course of the work, the influence of parameters such as the width and height of the printed layer, as well as the presence of cold seams between individual layers of the printed structure on the strength and bearing capacity of the forming elements of the permanent formwork was studied. The work execution is aimed at accelerating the introduction of advanced technologies in construction in terms of creating scientific and technical groundwork for the development of additive construction production and the development of the domestic regulatory and technical base in the field of construction 3D printing.

The Chemical Synthesis of the 1-C-Alkyl Substituted Pyrrolidine and Piperidine Iminosugar Natural Products and their Analogues

Abstract: A review of the chemical synthesis of the 1-C-alkyl substituted pyrrolidine and piperidine iminosugar natural products and their analogues (where the alkyl chain comprises two or more carbons) is provided. These syntheses can be grouped into nine different synthetic strategies that share a common approach toward installing the alkyl substituent. These include nucleophilic addition to aldimines; Grignard additions to glycosylamines, cyclic imides, and carbohydrates; Weinreb ketone synthesis; nucleophilic addition to cyclic nitrones; the Overmann rearrangement; the allylation of hemiaminals; and the Petasis borono-Mannich reaction. The broussonetine alkaloids have proven popular synthetic targets to develop new synthetic methods and verify these target molecules' structures and stereochemistry.

Comparison of ARIMA and ARIMA Error Regression Models: Evidence from the Russian Consumer Price Index

Consumer Price Index (CPI) is regarded as a common approach for measuring inflation. The present study examines the Russia CPI in the context of political and social upheavals, especially under the wars and the COVID-19. Due to the unstable political situation, the inflation rate in Russia sharply grow in 2014 and 2022 which creates two of the largest increase over the past decade after the Crimean war and the Ukrainian war were announced, thus it is crutial to make both long-term and short-term trends prediction. The paper aims to choose the best fitting model to estimate the future value of Russia monthly CPI data, and ultimately provides a suggestion and reference for monetary and fiscal authorities in Russia when making the policies to reduce inflation risk. The study applies the ARIMA (Autoregressive Integrated Moving Average) model to forecast Russia CPI, and uses regression with ARIMA errors to analyze the Russia CPI according to the ARIMA fitted values of the crude oil price in Europe. According to the result, ARIMA (2,3,1) is the best fitting model that can make comparatively sensible prediction for the future values.

From shady ditches to artificial pools – Does mire restoration benefit aquatic invertebrates?

To combat the loss of wetlands and the services they provide, many are now being restored. Rewetting, usually by ditch blocking, is a common approach to restore drained wetlands, where it can create a supply of permanent artificial pools. We studied how aquatic macroinvertebrate abundance, species richness and communities respond to ditch blocking, and how the new lentic waterbodies contribute to the habitat value of these ‘restored’ ecosystems. The study system was a large before-after-control-impact (BACI) experiment along forested mixotrophic bog edges, where we sampled macroinvertebrates prior to and after the restoration that had created a total of 308 individual pools along former ditch channels. The mean abundance and taxon richness increased similarly in drain-blocked and control ditches between 2015 and 2021 but blocked and control ditches started to host distinct communities. Most pronounced changes were the decrease in the numbers of bivalves and plecopterans and the appearance of vagile species, such as odonatans, hemipterans and certain beetles and flies to blocked ditches, including several characteristic species. Overall, the restoration provided new habitats for aquatic macroinvertebrates, but the convergence of these communities toward natural transitional mire communities remains to be studied.

HTA124 Can You Have a Common Approach to the Statistical Analysis Plan When Conducting Real-World Evidence Studies to Meet Multi-HTA Requirements?

HTA124 Can You Have a Common Approach to the Statistical Analysis Plan When Conducting Real-World Evidence Studies to Meet Multi-HTA Requirements?

The use of benzalkonium chloride in topical glaucoma treatment: An investigation of the efficacy and safety of benzalkonium chloride-preserved intraocular pressure-lowering eye drops and their effect on conjunctival goblet cells.

Glaucoma is a leading cause of the global prevalence of irreversible blindness. The pathogenesis of glaucoma is not entirely known, but the major risk factors include advancing age, genetic predisposition, and increased intraocular pressure (IOP). The only evidence-based treatment is a lowering of IOP through the use of eye drops, laser procedures, or surgical interventions. Although laser treatment is gaining recognition as a first-choice treatment option, the most common approach for managing glaucoma is IOP-lowering eye drops. A major challenge in the treatment is the occurrence of adverse events and poor adherence. In this context, the ocular surface is an area of great concern, as most glaucoma patients have dry eye disease (DED), which is largely caused by eye drops. Preservation with benzalkonium chloride (BAK) is a controversial topic due to its potential role as a significant cause of DED. A systematic review and meta-analyses investigate potential differences in efficacy and safety between BAK-preserved and BAK-free anti-glaucomatous eye drops (I). Many of the included studies report on ocular surface damage caused by the application of BAK-preserved eye drops. However, the meta-analyses addressing hyperemia, number of ocular adverse events, and tear break-up time did not identify any significant differences. The latter is likely due to varying measurement methods, different endpoints, and study durations. It is, therefore, possible that the large variations between the studies conceal differences in the safety profiles. The efficacy meta-analysis finds that there are no differences in the IOP-lowering effect between BAK-preserved and BAK-free eye drops, indicating that BAK is not necessary for the effectiveness of eye drops. To promote more homogeneous choices of endpoints and methods when evaluating BAK-preserved and BAK-free glaucoma treatments, a Delphi consensus statement was performed. In this study, glaucoma experts and ocular surface disease experts reached consensus on the key factors to consider when designing such studies (II). The hope is to have more studies with comparable endpoints that can systematically show the potentially adverse effects of BAK. The preclinical studies in the current Ph.D. research focus on conjunctival goblet cells (GCs). GCs are important for the ocular surface because they release the mucin MUC5AC, which is an essential component of the inner layer of the tear film. BAK preservation may damage the GCs and result in a low GC density, leading to an unstable tear film and DED. The most commonly used IOP-lowering drugs are prostaglandin analogs (PGAs). Thus, the conducted studies investigate the effect of PGAs preserved in different ways on GCs. BAK-preserved latanoprost is cytotoxic to primary cultured human conjunctival GCs and results in a scattered expression of MUC5AC, in contrast to negative controls, where MUC5AC is localized around the cell nucleus (III). Preservative-free (PF) latanoprost is not cytotoxic and does not affect the MUC5AC expression pattern. Furthermore, BAK-preserved travoprost is found to be cytotoxic in a time-dependent manner, while Polyquad®-preserved travoprost does not affect GC survival at any measured time point (IV). Both Polyquad and BAK induce scattered expression of MUC5AC. The cytotoxicity of BAK-preserved PGA eye drops is higher compared to the safer profile of PF and Polyquad-preserved PGA eye drops (V). Additionally, PF latanoprost does not increase the release of the inflammatory markers interleukin (IL)-6 and IL-8, unlike BAK-preserved latanoprost. A review highlights the active and inactive components of IOP-lowering eye drops (VI). Several preclinical and clinical studies have identified adverse effects of BAK. Although other components, such as the active drug and phosphates, can also cause adverse events, the review clearly states that BAK alone is a major source of decreased tolerability. The conclusion of this thesis is that BAK preservation is unnecessary and harmful to the ocular surface. The preclinical studies demonstrate that GCs die when exposed to BAK. Furthermore, they find that BAK induces a pro-inflammatory response. The review included in the thesis concludes that BAK should be phased out of eye drops for chronic use. Overall, the inclusion of BAK poses a risk of developing DED and poor adherence, which can ultimately lead to disease progression and blindness.

How principals identify low-performing teachers in public schools? Evidence from Chile

Abstract This research aims to explore how school principals determine whether they have low-performing teachers among their staff. Semi-structured interviews were conducted with 24 principals in Chilean public schools. The qualitative research entailed an inductive approach, along with an interview methodology and content analysis to investigate the research questions. Principals rely on three main sources of information to identify low performing teachers: classroom observations carried out by principals and senior leaders, parents’ complaints and students’ comments. However, there is no single common approach for identifying low-performing teachers, even within the same school district. This study is the first to report on low-performing teachers in Chile from the perspective of school principals.

Клинико-лабораторные предикторы тяжелой внебольничной пневмонии у детей до четырех лет

Community-acquired pneumonia (CAP) is a major cause of pediatric morbidity and mortality. Currently, there is no common approach to determination of CAP severity in children, which hampers early diagnosis and treatment of the disease. The study was aimed to determine clinical and laboratory predictors of severe CAP in children under 4 years of age. Analysis of clinical data, parameters of complete blood count (CBC), C-reactive protein (CRP) using nonparametric methods for hypothesis testing, univariate correlation analysis, cross-tabulation (Statistica 10.0), logistic regression, and ROC analysis (SPSS Statistics 20.0) was performed in 72 children aged 1 month to 3 years 11 months admitted to hospital due to CAP. Severe CAP was diagnosed in 16.7% of children. Causes of severe CAP included respiratory distress (moderate — 58.3%, severe — 16.7% of cases) and sepsis (25%). We identified significant clinical predictors of severe CAP: vomiting (OR 4.2), tachypnea (OR 28.3), chest wall retractions (OR 6), wheezing (OR 4), and the absence of rhinitis (OR 0.21). Isolated assessment of the CBC and CRP did not allow to predict CAP severity. We have developed a prediction model predicting severe CAP in children under 4 years of age based on the presence of rhinitis, tachypnea, as well as leukocyte count (sensitivity and specificity 91.7%). Thus, currently the main cause of severe CAP in children under 4 years of age is respiratory distress, in which wheezing predominates. Physical examination with an emphasis on detection of rhinitis and respiratory distress is essential for diagnosing severe CAP. The use of a pneumonia severity prediction model may contribute to improvement of management of CAP in patients under 4 years of age.

Improved Network-Calculus Nodal Delay-Bounds in Time-Sensitive Networks

In time-sensitive networks, bounds on worst-case delays are typically obtained by using network calculus and assuming that flows are constrained by bit-level arrival curves. However, in IEEE TSN or IETF DetNet, source flows are constrained on the number of packets rather than bits. A common approach to obtain a delay bound is to derive a bit-level arrival curve from a packet-level arrival curve. However, such a method is not tight: we show that better bounds can be obtained by directly exploiting the arrival curves expressed at the packet level. Our analysis method also obtains better bounds when flows are constrained with g-regulation, such as the recently proposed Length-Rate Quotient rule. It can also be used to generalize some recently proposed network-calculus delay-bounds for a service curve element with known transmission rate.

A Comparative Analysis of Optimization Algorithms for Finite Element Model Updating on Numerical and Experimental Benchmarks

Finite Element Model Updating (FEMU) is a common approach to model-based Non-Destructive Evaluation (NDE) and Structural Health Monitoring (SHM) of civil structures and infrastructures. Its application can be further utilized to produce effective digital twins of a permanently monitored structure. The FEMU concept, simple yet effective, involves calibrating and/or updating a numerical model based on the recorded dynamic response of the target system. This enables to indirectly estimate its material parameters, thus providing insight into its mass and stiffness distribution. In turn, this can be used to localize structural changes that may be induced by damage occurrence. However, several algorithms exist in the scientific literature for FEMU purposes. This study benchmarks three well-established global optimization techniques—namely, Generalized Pattern Search, Simulated Annealing, and a Genetic Algorithm application—against a proposed Bayesian sampling optimization algorithm. Although Bayesian optimization is a powerful yet efficient global optimization technique, especially suitable for expensive functions, it is seldom applied to model updating problems. The comparison is performed on numerical and experimental datasets based on one metallic truss structure built in the facilities of Cranfield University. The Bayesian sampling procedure showed high computational accuracy and efficiency, with a runtime of approximately half that of the alternative optimization strategies.

ICLR: Instance Credibility-Based Label Refinement for label noisy person re-identification

Person re-identification (Re-ID) has demonstrated remarkable performance when trained on accurately annotated data. However, in practical applications, the presence of annotation errors is unavoidable, which can undermine the accuracy and robustness of the Re-ID model training. To address the adverse impacts of label noise, especially in scenarios with limited training samples for each identity (ID), a common approach is to utilize all the available sample labels. Unfortunately, these labels contain incorrect labels, leading to the model being influenced by noise and compromising its performance. In this paper, we propose an Instance Credibility-based Label Refinement and Re-weighting (ICLR) framework to exploit partially credible labels to refine and re-weight incredible labels effectively. Specifically, the Label-Incredibility Optimization (LIO) module is proposed to optimize incredible labels before model training, which partitions the samples into credible and incredible samples and propagates credible labels to others. Furthermore, we design an Incredible Instance Re-weight (I2R) strategy, aiming to emphasize instances that contribute more significantly and dynamically adjust the weight of each instance. The proposed method seamlessly reinforces accuracy without requiring additional information or discarding any samples. Extensive experimental results conducted on Market-1501 and Duke-MTMC datasets demonstrate the effectiveness of our proposed method, leading to a substantial improvement in performance under both random noise and pattern noise settings. Code will be available at https://github.com/whut16/ReID-Label-Noise.

Moderate grazing weakens legacy effects of grazing history on primary productivity and biodiversity in a meadow grassland

Herbivore grazing has long-lasting effects on the structure and functions of grassland ecosystems, even after the grazing pressure has ceased. These lasting effects are known as grazing legacy effects and significantly affect plant productivity and biodiversity, with the magnitude increased with grazing intensity and further led to grassland degradation. Fencing is a common approach used for restoring degraded grasslands by excluding grazing. Moderate grazing representing a controlled level of grazing intensity is believed to be a useful way to restore ecological functions and thus mitigate grazing legacies. However, how fencing and ongoing moderate grazing affect grazing legacy effects remain unclear and rarely tested with field experiments, particularly with different grazing histories. Here, we investigated aboveground net primary productivity (ANPP), species richness, litter, soil organic carbon (SOC), soil dissolved nitrogen (DON) and hyphal length density (HLD) in 2015 (fencing for 1 year) and 2018 (fencing vs moderate grazing for the subsequent 3 years) after 0, 2, 5, 7 and 12 years of free grazing in a meadow steppe to explore how moderate grazing and fencing affect grazing legacy effects with different histories. We observed a decline in ANPP and species richness with increasing grazing histories, and the magnitude of grazing legacy effects increased accordingly. In most grazing histories, moderate grazing had a greater impact than fencing in reducing the grazing legacy effects on ANPP and species richness. However, with a grazing history of 12 years, moderate grazing led to a smaller increase in ANPP compared to fencing. Additionally, the grazing legacy effects on ANPP and species richness weakened with longer fencing duration for excluding grazing. Litter, SOC, DON and HLD exhibited consistent patterns with the dynamics of ANPP and species richness. Our results indicate that grazing legacy effects on ANPP and species richness intensify with prolonged grazing histories, suggesting that grazing history have stronger impacts on grassland ecosystem functions and structure. The study implies that moderate grazing is an effective strategy for restoring degraded grasslands, particularly for species richness and with short-term grazing histories, instead of relying solely on fencing. The changes of soil variables underlie the grazing legacy effects and the interactions with fencing and moderate grazing.

Numerical and experimental investigation of a high-static-low-dynamic stiffness vibration isolator with tunable stiffness and damping

Nonlinear vibration isolators that take advantage of negative stiffness elements can simultaneously meet the requirements of acceptable static deflection and frequency band of isolation and are thus superior to their linear counterparts. Incorporating electromagnetic elements into the vibration isolator is a common approach to provide the required negative stiffness. Electromagnetic mechanisms are low-cost, tuneable, and operate without friction. Also, they can extract or dampen the energy of the host system through electromechanical coupling. Therefore, electromechanical elements are ideal choices for designing multi-purpose vibration control mechanisms. In this study, three coils surrounded by arrays of permanent magnets are considered. When the system vibrates, their magnetic fields interact with that of a moving magnet attached to a rod supporting an isolated mass. The electromagnetic properties of the proposed system are simulated using the finite element method (FEM) and experimental measurements are used to validate the numerical simulations. Different scenarios for a system consisting of three coaxial coils are considered and compared, including powering the coils, open circuit coils, short circuit coils and coils shunted with an external circuit. Features of each case are discussed and general guidelines for designing a multipurpose vibration controller are given.

IoT-Edge Healthcare Solutions Empowered by Machine Learning

Managing the overwhelming volume of data collected by medical sensors presents a challenge in extracting relevant insights. This paper advocates for the development of an algorithm tailored to body sensor networks to identify outliers in collected data. Leveraging machine learning and statistical sampling methodologies, this research aims to optimize real-time response, particularly as computational tasks migrate to backend systems. Addressing the increasing dispersion of computing power across various domains, this study highlights the potential bottleneck posed by computation as Internet-of-Things (IoT) devices proliferate. To mitigate battery drain, a common approach involves offloading processing to background servers. However, the widespread adoption of IoT devices has sparked concerns about privacy and security. Current measures are deemed insufficient in light of escalating cyber threats. Machine learning methods offer promise in identifying vulnerabilities within IoT systems. Edge computing emerges as a solution to enhance network response times, decentralization, and security. By leveraging distributed-edge computing within an IoT framework, this paper investigates the fusion of cloud and edge computing with machine learning. Specifically, it explores how these technologies can be harnessed in the medical field, utilizing sensor-equipped IoT devices to collect extensive data for analysis. The proposed approach involves proactive decision-making at the front end, guided by an IoT server, and employs machine learning algorithms at the backend to identify pertinent data signatures. This paper underscores the significance of combining cloud, edge computing, and machine learning in a distributed-edge-computing-based IoT framework, offering a potential avenue for real-time, efficient solutions in various domains.

Determination of road surface correction factors for CNOSSOS-EU in Ireland

Transport Infrastructure Ireland (TII) is a state agency in the Republic of Ireland. The primary function of TII is to provide an integrated approach to the future development and operation of the national roads network and light rail infrastructure throughout Ireland. TII are designated as the Noise Mapping Body (NMB) responsible for the development of strategic noise maps for all national roads carrying in excess of 3 million vehicles a year. Directive 2002/49/EC (commonly referred to as the Environment Noise Directive, 'the END') relates to the assessment and management of environmental noise. It is the main instrument of the European Union (EU) to quantify noise pollution levels, and trigger action within both Member States and at EU level. The END has the aim of establishing a common approach to the management of noise within the EU. Commission Directive (EU) 2015/996 established the common noise assessment methodology for Europe (CNOSSOS-EU). The CNOSSOS-EU road traffic noise emission model calculates rolling noise for different vehicle categories as a function of speed and frequency. The emission is initially calculated under reference conditions, then corrected for differing situations. For road surfaces which do not align with the reference surface, road surface correction factors should ideally be developed, otherwise a number of default values are provided within CNOSSOS-EU which are taken from the Dutch national noise assessment method, Rmg2012, and therefore contain only surfaces that are common in the Netherlands. For the implementation of CNOSSOS-EU in Ireland, a project was undertaken to develop road surface corrections for at least the three most common pavement types on the TII network with no direct match to the CNOSSOS-EU database. This paper will describe the measurements taken on Irish roads, the resulting road surface corrections, and our conclusions and recommendations for using the road surface corrections.

Speed kills? Quantifying the association between police traffic stops, types of stops, and motor vehicle collisions

BackgroundMotor vehicle crashes (MVCs) are a leading cause of traumatic death and injury. Police traffic stops (PTS) are a common approach to enforcing motor vehicle laws intended to prevent MVCs. However, it is unclear which types of PTS are most effective. This study examined the relationship of PTS subtypes among municipal police patrols on non-interstate roads and MVCs and MVC-related deaths. MethodsPTS subtype data were characterized from six North Carolina cities: Charlotte, Durham, Fayetteville, Greensboro, Raleigh, and Winston-Salem. The primary outcomes of this study were yearly non-interstate MVC and MVC-related death rates per 100 population. The data were analyzed as balanced time-series cross-sectional data. The statistical analysis accounted for time-dependent and city-dependent confounding. We used a two-way fixed effects model to analyze the relationship between PTS and MVC or MVC-related deaths. We also utilized the difference in difference (DID) analysis to analyze if the reduction of PTS following a 2012 policing administrative change in Fayetteville had an association with MVC or MVC-related deaths. ResultsWe found no significant overall association between non-interstate PTS and MVCs (Coeff: -0.00006; p = 0.43) or MVC-related deaths (Coeff: -0.00011; p = 0.15). Panel regression suggested no significant relationship between MVCs and MVC-related deaths and PTS related to driving while impaired (p = 0.36), safe movement violation (p = 0.43), or seatbelt violations (p = 0.17). However, speed limit violations (Coeff: -0.00025; p = 0.032) and stop-light/sign violations (Coeff: -0.00147; p = 0.017) related to PTS significantly reduced MVC-related deaths. The DID regression showed no significant impact on MVCs (p = 0.924) or MVC-related deaths (0.706) before and after the police reform period. ConclusionsThe evidence regarding the absence of an overall association and any association with most PTS subtypes suggest that PTS are not effective for MVC death prevention. Policymakers may proceed with exploring modifications to policing efforts without detriments to public safety as defined by MVC and MVC-related deaths. Level of evidenceRetrospective epidemiological study, level IV

Flow rate quantification of small methane leaks using laser spectroscopy and deep learning

To ensure safety in the transport and loading processes involving hazardous gasses, the detection of gas leaks in technical installations and the subsequent quantification of the released mass flows is of great public interest. One common approach to obtain this information is the usage of gas cameras, but they typically provide only inaccurate concentration data and are not sensitive enough to detect small gas leaks. In this work, we use a new gas camera approach that is based on laser absorption spectroscopy combined with mid-infrared imaging to solve these challenges. We gain videos of artificial gas leaks that containing more accurate concentration length information in ppm*m while visualizing small flow rates between 5 and 60 ml/min. For the ease of experimentation, we utilize methane while the camera can be adapted to other possibly hazardous gasses. On the dataset obtained, we train a deep-learning model aiming to continuously predict the flowrate. Additionally, we evaluate it on an eight-class classification task to compare accuracy metrics. We demonstrate an average difference of 1.6 ml/min from the mean predicted values to the true flow rates as well as an accuracy of (54 ± 2) % in the classification task.

Predictive Dispatch of Volunteer First Responders: Algorithm Development and Validation.

Smartphone-based emergency response apps are increasingly being used to identify and dispatch volunteer first responders (VFRs) to medical emergencies to provide faster first aid, which is associated with better prognoses. Volunteers' availability and willingness to respond are uncertain, leading in recent studies to response rates of 17% to 47%. Dispatch algorithms that select volunteers based on their estimated time of arrival (ETA) without considering the likelihood of response may be suboptimal due to a large percentage of alerts wasted on VFRs with shorter ETA but a low likelihood of response, resulting in delays until a volunteer who will actually respond can be dispatched. This study aims to improve the decision-making process of human emergency medical services dispatchers and autonomous dispatch algorithms by presenting a novel approach for predicting whether a VFR will respond to or ignore a given alert. We developed and compared 4 analytical models to predict VFRs' response behaviors based on emergency event characteristics, volunteers' demographic data and previous experience, and condition-specific parameters. We tested these 4 models using 4 different algorithms applied on actual demographic and response data from a 12-month study of 112 VFRs who received 993 alerts to respond to 188 opioid overdose emergencies. Model 4 used an additional dynamically updated synthetic dichotomous variable, frequent responder, which reflects the responder's previous behavior. The highest accuracy (260/329, 79.1%) of prediction that a VFR will ignore an alert was achieved by 2 models that used events data, VFRs' demographic data, and their previous response experience, with slightly better overall accuracy (248/329, 75.4%) for model 4, which used the frequent responder indicator. Another model that used events data and VFRs' previous experience but did not use demographic data provided a high-accuracy prediction (277/329, 84.2%) of ignored alerts but a low-accuracy prediction (153/329, 46.5%) of responded alerts. The accuracy of the model that used events data only was unacceptably low. The J48 decision tree algorithm provided the best accuracy. VFR dispatch has evolved in the last decades, thanks to technological advances and a better understanding of VFR management. The dispatch of substitute responders is a common approach in VFR systems. Predicting the response behavior of candidate responders in advance of dispatch can allow any VFR system to choose the best possible response candidates based not only on ETA but also on the probability of actual response. The integration of the probability to respond into the dispatch algorithm constitutes a new generation of individual dispatch, making this one of the first studies to harness the power of predictive analytics for VFR dispatch. Our findings can help VFR network administrators in their continual efforts to improve the response times of their networks and to save lives.