Data Collection Protocol Research Articles

Facial and Emotion Recognition Deficits in Myasthenia Gravis.

Myasthenia gravis (MG) is a neuromuscular disease of autoimmune etiology and chronic evolution. In addition to the muscle weakness and fatigue that characterize MG, in some studies patients show an inferior performance in cognitive tasks and difficulties in recognizing basic emotions from facial expressions. However, it remains unclear if these difficulties are due to anxious-depressive symptoms that these patients present or related to cognitive abilities, such as facial recognition. This study had a descriptive cross-sectional design with a sample of 92 participants, 52 patients with MG and 40 healthy controls. The data collection protocol included measures to assess recognition of facial expressions (BRFT), facial emotional expression (FEEL), and levels of anxiety and depression (HADS). The MG group had worse performance than the control group in recognizing "fear" (p = 0.001; r = 0.344), "happiness" (p = 0.000; r = 0.580), "disgust" (p = 0.000; r = 0.399), "surprise" (p = 0.000; r = 0.602), and "anger" (p = 0.007; r = 0.284). Likewise, the MG group also underperformed in facial recognition (p = 0.001; r = 0.338). These difficulties were not related to their levels of anxiety and depression. Alterations were observed both in the recognition of facial emotions and in facial recognition, without being mediated by emotional variables. These difficulties can influence the interpersonal interaction of patients with MG.

Continuous Monitoring of Heart Rate Variability in Free-Living Conditions Using Wearable Sensors: Exploratory Observational Study.

Wearable physiological monitoring devices are promising tools for remote monitoring and early detection of potential health changes of interest. The widespread adoption of such an approach across communities and over long periods of time will require an automated data platform for collecting, processing, and analyzing relevant health information. In this study, we explore prospective monitoring of individual health through an automated data collection, metrics extraction, and health anomaly analysis pipeline in free-living conditions over a continuous monitoring period of several months with a focus on viral respiratory infections, such as influenza or COVID-19. A total of 59 participants provided smartwatch data and health symptom and illness reports daily over an 8-month window. Physiological and activity data from photoplethysmography sensors, including high-resolution interbeat interval (IBI) and step counts, were uploaded directly from Garmin Fenix 6 smartwatches and processed automatically in the cloud using a stand-alone, open-source analytical engine. Health risk scores were computed based on a deviation in heart rate and heart rate variability metrics from each individual's activity-matched baseline values, and scores exceeding a predefined threshold were checked for corresponding symptoms or illness reports. Conversely, reports of viral respiratory illnesses in health survey responses were also checked for corresponding changes in health risk scores to qualitatively assess the risk score as an indicator of acute respiratory health anomalies. The median average percentage of sensor data provided per day indicating smartwatch wear compliance was 70%, and survey responses indicating health reporting compliance was 46%. A total of 29 elevated health risk scores were detected, of which 12 (41%) had concurrent survey data and indicated a health symptom or illness. A total of 21 influenza or COVID-19 illnesses were reported by study participants; 9 (43%) of these reports had concurrent smartwatch data, of which 6 (67%) had an increase in health risk score. We demonstrate a protocol for data collection, extraction of heart rate and heart rate variability metrics, and prospective analysis that is compatible with near real-time health assessment using wearable sensors for continuous monitoring. The modular platform for data collection and analysis allows for a choice of different wearable sensors and algorithms. Here, we demonstrate its implementation in the collection of high-fidelity IBI data from Garmin Fenix 6 smartwatches worn by individuals in free-living conditions, and the prospective, near real-time analysis of the data, culminating in the calculation of health risk scores. To our knowledge, this study demonstrates for the first time the feasibility of measuring high-resolution heart IBI and step count using smartwatches in near real time for respiratory illness detection over a long-term monitoring period in free-living conditions.

The use of online methods to recruit and follow a hard-to-reach population in the Peer Alternatives for Addiction Study 2021 Cohort.

Although studies are increasingly adopting online protocols, few such studies in the addiction field have comprehensively described their data review procedures and successes in detecting low-quality/fraudulent data. The current study describes data collection protocols and outcomes of a large, longitudinal study (the PAL Study 2021) that implemented online design elements to study individuals seeking peer support for an alcohol use disorder. In 2021, the PAL Study collaborated with mutual-help group (MHG) partners and recovery-related organizations to recruit individuals attending a 12-step group, Women for Sobriety (WFS), LifeRing Secular Recovery, and/or SMART Recovery for an alcohol problem in-person and/or online in the prior 30 days. Participation was solicited both online and in-person. Individuals accessed baseline surveys via an open web link; follow-ups occurred at 6 and 12 months. Analyses included calculating the proportion of surveys eliminated in data quality review; comparing MHG subsamples to internal survey (benchmark) data for Alcoholics Anonymous (AA), WFS, LifeRing, and SMART; and examining response rates and attrition. Although 93% of respondents who opened the baseline survey completed it, 87% of baseline surveys were eliminated in data quality review (final N = 531). Nonetheless, cleaned MHG subsamples were generally similar to benchmark samples on gender, age, race/ethnicity, and education. Follow-up rates for the cleaned sample were 88% (6 months) and 85% (12 months). Analyses revealed some differences in attrition by gender, primary MHG, and lifetime drug problems, but there was no evidence of greater attrition among those in earlier/less stable recovery. Study methods appear to have produced a valid, largely representative sample of the hard-to-reach target population that was successfully followed across 12 months. However, given the high survey elimination rate and need for extensive data review, we recommend that researchers avoid open-link designs and include comprehensive data review when incorporating online design elements.

Role of Gender in Health and Disease: Methods of Reporting and Interactions with Sex and Other Factors.

Sex and gender are distinct terms that must be used correctly. Data regarding sex and gender may be collected using a 2-step method that separates biological sex and self-reported gender identity. The PhenX Toolkit, funded by the National Institutes of Health (NIH), is one tool that provides investigators with recommended standard data-collection protocols. Another tool is the Diversity Minimal Item Set questionnaire. Importantly, sex and gender interact: for example, pain has both biological aspects (sex differences in electrical, ischemic, thermal, pressure, and muscle pain sensitivity) and cultural aspects (gender factors in how people report pain and how physicians understand and treat pain in patients). Gender norms, identity, and relations all impact patient care. Gender norms, for instance, may influence how a person experiences pain, gender identity may influence a person's willingness to report pain, and gender relations may influence a physician's gendered expectations in relation to a patient's gendered behaviors. Clinicians may perceive women's pain to be psychological; as a result, women may receive more nonspecific diagnoses, wait longer for treatment, and receive more antidepressants and fewer analgesics than men. Research on gender-diverse people and pain is just now emerging. Resources for methods of reporting include The Lancet, Nature, and the Sex and Gender Equity in Research (SAGER) Guidelines. We must consider all relevant factors intersecting with sex and gender, including age, disabilities, educational background, ethnicity, family configuration, geographic location, race, sexuality, social and economic status, sustainability, and more.

Burden and Inattentive Responding in a 12-Month Intensive Longitudinal Study: Interview Study Among Young Adults.

Intensive longitudinal data (ILD) collection methods have gained popularity in social and behavioral research as a tool to better understand behavior and experiences over time with reduced recall bias. Engaging participants in these studies over multiple months and ensuring high data quality are crucial but challenging due to the potential burden of repeated measurements. It is suspected that participants may engage in inattentive responding (IR) behavior to combat burden, but the processes underlying this behavior are unclear as previous studies have focused on the barriers to compliance rather than the barriers to providing high-quality data. This study aims to broaden researchers' knowledge about IR during ILD studies using qualitative analysis and uncover the underlying IR processes to aid future hypothesis generation. We explored the process of IR by conducting semistructured qualitative exit interviews with 31 young adult participants (aged 18-29 years) who completed a 12-month ILD health behavior study with daily evening smartphone-based ecological momentary assessment (EMA) surveys and 4-day waves of hourly EMA surveys. The interviews assessed participants' motivations, the impact of time-varying contexts, changes in motivation and response patterns over time, and perceptions of attention check questions (ACQs) to understand participants' response patterns and potential factors leading to IR. Thematic analysis revealed 5 overarching themes on factors that influence participant engagement: (1) friends and family also had to tolerate the frequent surveys, (2) participants tried to respond to surveys quickly, (3) the repetitive nature of surveys led to neutral responses, (4) ACQs within the surveys helped to combat overly consistent response patterns, and (5) different motivations for answering the surveys may have led to different levels of data quality. This study aimed to examine participants' perceptions of the quality of data provided in an ILD study to contribute to the field's understanding of engagement. These findings provide insights into the complex process of IR and participant engagement in ILD studies with EMA. The study identified 5 factors influencing IR that could guide future research to improve EMA survey design. The identified themes offer practical implications for researchers and study designers, including the importance of considering social context, the consideration of dynamic motivations, and the potential benefit of including ACQs as a technique to reduce IR and leveraging the intrinsic motivators of participants. By incorporating these insights, researchers might maximize the scientific value of their multimonth ILD studies through better data collection protocols. RR2-10.2196/36666.

Telephone-Based Psychological Care During the 2020 Lockdown in Spain and Protocol for Data Collection.

During the COVID-19 lockdown in 2020, the General Council of Psychology in Spain, together with the regional Official Colleges of Psychology, launched the Psychological Care Telephone Program (PCTP) to provide mental health services to the population. The aim of the present study was to perform a descriptive analysis of the PCTP by analysing the data collected during the lockdown and at the 12-month follow-up, and to develop a brief protocol designed to standardise data collection procedures. A total of 10,119 inbound telephone calls were made to the PCTP from March to May 2020, and 337 follow-up calls at 12 months. The most common reasons for contacting the PCTP were to consult for symptoms of anxiety (66.8%), depression (30.5%), and/or family problems (13.9%). At the 12-month follow-up, many users experienced anxiety (38%), depressive (35%), and panic (34%) symptoms. More than half of users reported using psychopharmacological medicines. This study demonstrates the need to offer the population telephone-based mental health consultations during times of crisis. It also shows the importance of systematising intervention and data collection procedures for future crises. We propose a data collection protocol for use with emergency telephone psychological assistance programmes.

Strengthening Collaborative Community and Research Partnership Decision-Making Processes to Achieve Health Equity

Collaborative community and academic partner decision-making is imperative for efforts to reduce structural racism and achieve health equity. It is essential to intentionally integrate methods and tools that will help ensure efficient, collaborative, and clear decision-making among community and research partners. The RAPID® decision-making framework is one of these tools, and it is an acronym that stands for Recommend, Agree, Perform, Input, and Decide. RAPID® assigns roles in the decision-making process to explicitly clarify who provides input or feedback on a decision, who decides, and who implements the decision. The RAPID® allows diverse, vital partners to share their expertise and provide direction at critical moments during the research process. This collaborative process activates community power and ensures that the research questions will benefit the communities we seek to serve. Our brief details a case example of how we used the RAPID® to co-create and revise data collection protocols and instruments for a research project to understand how community benefits agreements disrupt the pathways from residential segregation to health disparities in disinvested communities. We will share what we learned from implementing the RAPID® and recommendations for future adoption

Software advancements in automatic epilepsy diagnosis and seizure detection: 10-year review

Epilepsy is a chronic neurological disorder that may be diagnosed and monitored using routine diagnostic tests like Electroencephalography (EEG). However, manual introspection and analysis of EEG signals is presently difficult and repetitive task even for experienced neuro-technologists with high false-positive rates and inter- and intra-rater reliability. Software advancements using Artificial Intelligence (AI) algorithms have the potential to early detect and predict abnormal patterns observed in EEG signals. The present review focuses on systematically reporting software advancements and their implementation using hardware systems in automatic epilepsy diagnosis and seizure detection for the past 10 years. Traditional, hybrid, and end-to-end AI-based pipelines and associated EEG datasets have been discussed. The review summarizes and compares reported articles, datasets, and patents through various subjective and objective parameters in this field. Latest advancements demonstrate that AI-based pipelines can reduce the introspection time by at least 50% without compromising the diagnostic accuracy or abnormal event detection. A significant rise in hardware implementation of software-based pipelines, end-to-end deep learning architectures for real-time analysis, and granted patents has been noticed since 2011. More than twenty-eight datasets have been developed to automatically diagnose epileptic EEG signals from 2001 to 2023. Extensive analysis using explainability tools, cross-dataset generalizations, reproducibility analysis, and ablation experiments can further improve the existing AI-based pipelines in this field. There is a need for the development of standardized protocols for data collection and its AI pipeline for a robust, inter- and intra-rater reliability-free, and real-time automatic epilepsy diagnosis.

Real-Time Sensor-Based Human Activity Recognition for eFitness and eHealth Platforms

Human Activity Recognition (HAR) plays an important role in the automation of various tasks related to activity tracking in such areas as healthcare and eldercare (telerehabilitation, telemonitoring), security, ergonomics, entertainment (fitness, sports promotion, human–computer interaction, video games), and intelligent environments. This paper tackles the problem of real-time recognition and repetition counting of 12 types of exercises performed during athletic workouts. Our approach is based on the deep neural network model fed by the signal from a 9-axis motion sensor (IMU) placed on the chest. The model can be run on mobile platforms (iOS, Android). We discuss design requirements for the system and their impact on data collection protocols. We present architecture based on an encoder pretrained with contrastive learning. Compared to end-to-end training, the presented approach significantly improves the developed model’s quality in terms of accuracy (F1 score, MAPE) and robustness (false-positive rate) during background activity. We make the AIDLAB-HAR dataset publicly available to encourage further research.

A site-wise reliability analysis of the ABCD diffusion fractional anisotropy and cortical thickness: impact of scanner platforms.

The Adolescent Brain and Cognitive Development (ABCD) project is the largest study of adolescent brain development. ABCD longitudinally tracks 11,868 participants aged 9-10 years from 21 sites using standardized protocols for multi-site MRI data collection and analysis. While the multi-site and multi-scanner study design enhances the robustness and generalizability of analysis results, it may also introduce non-biological variances including scanner-related variations, subject motion, and deviations from protocols. ABCD imaging data were collected biennially within a period of ongoing maturation in cortical thickness and integrity of cerebral white matter. These changes can bias the classical test-retest methodologies, such as intraclass correlation coefficients (ICC). We developed a site-wise adaptive ICC (AICC) to evaluate the reliability of imaging-derived phenotypes while accounting for ongoing brain development. AICC iteratively estimates the population-level age-related brain development trajectory using a weighted mixed model and updates age-corrected site-wise reliability until convergence. We evaluated the test-retest reliability of regional fractional anisotropy (FA) measures from diffusion tensor imaging and cortical thickness (CT) from structural MRI data for each site. The mean AICC for 20 FA tracts across sites was 0.61±0.19, lower than the mean AICC for CT in 34 regions across sites, 0.76±0.12. Remarkably, sites using Siemens scanners consistently showed significantly higher AICC values compared to those using GE/Philips scanners for both FA (AICC=0.71±0.12 vs 0.46±0.17, p<0.001) and CT (AICC=0.80±0.10 vs 0.69±0.11, p<0.001). These findings demonstrate site-and-scanner related variations in data quality and underscore the necessity for meticulous data curation in subsequent association analyses.

Temporal-Posterior Alpha Power in Resting-State Electroencephalography as a Potential Marker of Complex Childhood Trauma in Institutionalized Adolescents.

The present study explored whether, given the association of temporal alpha with fear circuitry (learning and conditioning), exposure to complex childhood trauma (CCT) is reflected in the temporal-posterior alpha power in resting-state electroencephalography (EEG) in complex trauma-exposed adolescents in a sample of 25 adolescents and similar controls aged 12-17 years. Both trauma and psychopathology were screened or assessed, and resting-state EEG was recorded following a preregistered protocol for data collection. Temporal-posterior alpha power, corresponding to the T5 and T6 electrode locations (international 10-20 system), was extracted from resting-state EEG in both eyes-open and eyes-closed conditions. We found that in the eyes-open condition, temporal-posterior alpha was significantly lower in adolescents exposed to CCT relative to healthy controls, suggesting that childhood trauma exposure may have a measurable impact on alpha oscillatory patterns. Our study highlights the importance of considering potential neural markers, such as temporal-posterior alpha power, to understanding the long-term consequences of CCT exposure in developmental samples, with possible important clinical implications in guiding neuroregulation interventions.

Physiological Profiling of Agitation in Dementia: Insights From Wearable Sensor Data.

The number of people with dementia is expected to triple to 152 million in 2050, with 90% having accompanying behavioral and psychological symptoms (BPSD). Agitation is among the most critical BPSD and can lead to decreased quality of life for people with dementia and their caregivers. This study aims to explore objective quantification of agitation in people with dementia by analyzing the relationships between physiological and movement data from wearables and observational measures of agitation. The data presented here is from 30 people with dementia, each included for 1 week, collected following our previously published multimodal data collection protocol. This observational protocol has a cross-sectional repeated measures design, encompassing data from both wearable and fixed sensors. Generalized linear mixed models were used to quantify the relationship between data from different wearable sensor modalities and agitation, as well as motor and verbal agitation specifically. Several features from wearable data are significantly associated with agitation, at least the p < .05 level (absolute β: 0.224-0.753). Additionally, different features are informative depending on the agitation type or the patient the data were collected from. Adding context with key confounding variables (time of day, movement, and temperature) allows for a clearer interpretation of feature differences when a person with dementia is agitated. The features shown to be significantly different, across the study population, suggest possible autonomic nervous system activation when agitated. Differences when splitting the data by agitation type point toward a need for future detection models to tailor to the primary type of agitation expressed. Finally, patient-specific differences in features indicate a need for patient- or group-level model personalization. The findings reported in this study both reinforce and add to the fundamental understanding of and can be used to drive the objective quantification of agitation.

Evaluation of Bitemark Analysis's Potential Application in Forensic Identification: A Systematic Review.

to investigate the available literature systematically and evaluate the scientific evidence published over the past decade concerning the potential application of bitemark analysis in forensic identification. Two researchers meticulously searched electronic databases from January 2012 to December 2023, including Scopus, PubMed, Web of Science, and the Cochrane Library. Adhering to the PRISMA statement guidelines, this review employed appropriate medical subject headings (MeSHs) and free-text synonyms. Strict inclusion and exclusion criteria were applied during article retrieval. The findings yielded controversial outcomes. Approximately two-thirds of the articles concluded that bitemark analysis is useful in forensic identification, while the remaining articles did not report statistically significant outcomes and cautioned against relying solely on bitemark analysis for identification. The authors assert that bitemark analysis can be a reliable and complementary method for forensic identification, contingent upon the establishment and adoption of a universally accepted global protocol for data collection, processing, and interpretation. Undoubtedly, recent years have witnessed a notable increase in research focused on bitemark identification, driven by the goal of achieving quantitative, objective, reproducible, and accurate results.

Strategies for involving patients and the public in scaling initiatives in health and social services: A scoping review.

Scaling in health and social services (HSS) aims to increase the intended impact of proven effective interventions. Patient and public involvement (PPI) is critical for ensuring that scaling beneficiaries' interests are served. We aimed to identify PPI strategies and their characteristics in the science and practice of scaling in HSS. In this scoping review, we included any scaling initiative in HSS that used PPI strategies and reported PPI methods and outcomes. We searched electronic databases (e.g., Medline) from inception to 5 February 2024, and grey literature (e.g., Google). Paired reviewers independently selected and extracted eligible reports. A narrative synthesis was performed and we used the PRISMA for Scoping Reviews and the Guidance for Reporting Involvement of Patients and the Public (GRIPP2). We included 110 unique reports out of 24,579 records. In the past 5 years, the evidence on PPI in scaling has increased faster than in any previous period. We found 236 mutually nonexclusive PPI strategies among 120 scaling initiatives. Twenty-four initiatives did not target a specific country; but most of those that did so (n = 96) occurred in higher-income countries (n = 51). Community-based primary health care was the most frequent level of care (n = 103). Mostly, patients and the public were involved throughout all scaling phases (n = 46) and throughout the continuum of collaboration (n = 45); the most frequently reported ethical lens regarding the rationale for PPI was consequentialist-utilitarian (n = 96). Few papers reported PPI recruitment processes (n = 31) or incentives used (n = 18). PPI strategies occurred mostly in direct care (n = 88). Patient and public education was the PPI strategy most reported (n = 31), followed by population consultations (n = 30). PPI in scaling is increasing in HSS. Further investigation is needed to better document the PPI experience in scaling and ensure that it occurs in a meaningful and equitable way. Two patients were involved in this review. They shared decisions on review questions, data collection instruments, protocol design, and findings dissemination. Open Science Framework on 19 August 2020 (https://osf.io/zqpx7/).

SMART quality control analysis of pavement condition data for pavement management applications

Assuring the accuracy and reliability of pavement condition data is crucial for effective decision-making in pavement management. Despite existing data collection protocols, concerns persist regarding data quality. This paper introduces SMART, a systematic statistical method designed to analyze the quality of pavement condition data from field surveys for pavement management applications. SMART employs a structured procedure that expands traditional descriptive statistics by applying interrater reliability statistics combined with bootstrapping methods and Modified Blant Altman diagrams to evaluate data quality. A comparative analysis of interrater statistics, including Cohen’s Kappa (CK), Interclass Correlation (IC), Krippendorff’s Alpha (KA), Percent Agreement (PA), and Weighted Cohen’s Kappa (WCK), is conducted in the research study. As a result, the adoption of KA and Modified Bland-Altman diagrams for data analysis is recommended. KA demonstrates versatility across diverse data types, accommodating nominal, ordinal, interval, and ratio-level data, while Modified Bland-Altman diagrams facilitate data dispersion analysis to visualize possible bias trends for the condition ratings. A case study is presented to demonstrate the applicability of SMART to analyzing Pavement Condition Index (PCI) data provided by the Metropolitan Transportation Commission (MTC) in California. This methodological approach aims to enhance pavement management decisions by ensuring the reliability of condition field survey data through the implementation of robust analytical quality control procedures.

Rehospitalization of preterm infants below 32 weeks of gestational age in the first 2 years post discharge

To describe the frequency and characteristics of readmissions in preterm infants younger than 32 weeks of GA or weighing less than 1500 g (< 32w/< 1500 g) at 2 years post-discharge from neonatology. Retrospective observational study of a cohort of newborns < 32w/< 1500 g discharged from a public health care center (2009-2017). The frequency, time of occurrence, risk factors, causes, and severity of hospital readmissions were analyzed. The respective perinatal characteristics and subsequent readmissions were described. The Ethics Committee approved the data collection protocol. 989 newborns < 32w/< 1500 g were included; 410 (41.5%) were readmitted at least once before the age of 2 years, equivalent to 686 episodes (1.7/child); 129 children (31.4%) were admitted to the Pediatric Intensive Care Unit (PICU), with a mean length of stay of 7.7 days. The greatest risk for hospital readmission was during the first 6 months post-discharge. The main cause was respiratory (70%) and respiratory syncytial virus was the most frequent germ. The risk factors associated with readmission due to respiratory causes were bronchopulmonary dysplasia (BPD) (OR: 1.73; 95%CI: 1.26-2.37) and number of siblings (OR: 1.18; 95%CI: 1.04-1.33). Newborns < 32s/< 1500 g are at high risk of hospital readmission due to respiratory causes and PICU admission in the first months post-discharge; BPD and number of siblings were the main risk factors.

An interventional cross-sectional study of electrolyte levels in newborns with hyperbilirubinemia before and after phototherapy in a tertiary care hospital in central India

Introduction This study protocol outlines a comprehensive investigation into the impact of phototherapy on electrolyte levels in newborns with hyperbilirubinemia. With a focus on neonates admitted to the Neonatal Intensive Care Unit (NICU) at Jawaharlal Nehru Medical College and AVBR Hospital, Sawangi, Wardha, Central India, the research aims to contribute valuable insights into the physiological changes associated with this common neonatal condition. The study’s introduction highlights the rationale, significance, and gaps in current knowledge, emphasising the need for a detailed exploration of electrolyte dynamics before and after phototherapy. Method The methodology involves a descriptive, interventional cross-sectional design with a calculated sample size of 264 neonates. Standardised protocols for data collection, including serum electrolyte analysis and urine-specific gravity assessment, will be employed. Inclusion and exclusion criteria are clearly defined to ensure a homogenous study population. Statistical analyses, utilising R Studio 4.3.1 will encompass descriptive statistics, comparative analysis, correlation analysis, and multivariate analysis to explore the nuanced relationships between variables. Rigorous ethical considerations and transparency in reporting will guide the data collection process. Expected Result Anticipated outcomes include a nuanced understanding of changes in serum electrolyte levels following phototherapy and the correlation of these changes with the treatment duration. The study is poised to shed light on the impact of gestational age and birth weight on electrolyte responses. Through rigorous statistical analysis, the research aims to provide evidence-based insights that can inform neonatal care protocols. The anticipated findings hold the potential to influence clinical practices, enhancing the quality of care provided to newborns with hyperbilirubinemia undergoing phototherapy.

An appraisal of statistical and probabilistic models in highway pavements

Accurate performance prediction is crucial for safe and efficient travel on highway pavements. Within pavement engineering, statistical models play a pivotal role in understanding pavement behavior and durability. This comprehensive study critically evaluates a spectrum of statistical models utilized in pavement engineering, encompassing mechanistic-empirical, Weibull distribution, Markov chain, regression, Bayesian networks, Monte Carlo simulation, artificial neural networks, support vector machines, random forest, decision tree, fuzzy logic, time series analysis, stochastic differential equations, copula, hidden semi-Markov, generalized linear, survival analysis, response surface methodology and extreme value theory models. The assessment meticulously examines equations, parameters, data prerequisites, advantages, limitations, and applicability of each model. Detailed discussions delve into the significance of equations and parameters, evaluating model performance in predicting pavement distress, performance assessment, design optimization, and life-cycle cost analysis. Key findings emphasize the critical aspects of accurate input parameters, calibration, validation, data availability, and model complexity. Strengths, limitations, and applicability across various pavement types, materials, and climate conditions are meticulously highlighted for each model. Recommendations are outlined to enhance the effectiveness of statistical models in pavement engineering. These suggestions encompass further research and development, standardized data collection, calibration and validation protocols, model integration, decision-making frameworks, collaborative efforts, and ongoing model evaluation. Implementing these recommendations is anticipated to enhance prediction accuracy and enable informed decision-making throughout highway pavement design, construction, maintenance, and management. This study is anticipated to serve as a valuable resource, providing guidance and insights for researchers, practitioners, and stakeholders engaged in asphalt engineering, facilitating the effective utilization of statistical models in real-world pavement projects.

CentralBark Image Dataset and Tree Species Classification Using Deep Learning

The task of tree species classification through deep learning has been challenging for the forestry community, and the lack of standardized datasets has hindered further progress. Our work presents a solution in the form of a large bark image dataset called CentralBark, which enhances the deep learning-based tree species classification. Additionally, we have laid out an efficient and repeatable data collection protocol to assist future works in an organized manner. The dataset contains images of 25 central hardwood and Appalachian region tree species, with over 19,000 images of varying diameters, light, and moisture conditions. We tested 25 species: elm, oak, American basswood, American beech, American elm, American sycamore, bitternut hickory, black cherry, black locust, black oak, black walnut, eastern cottonwood, hackberry, honey locust, northern red oak, Ohio buckeye, Osage-orange, pignut hickory, sassafras, shagbark hickory silver maple, slippery elm, sugar maple, sweetgum, white ash, white oak, and yellow poplar. Our experiment involved testing three different models to assess the feasibility of species classification using unaltered and uncropped images during the species-classification training process. We achieved an overall accuracy of 83.21% using the EfficientNet-b3 model, which was the best of the three models (EfficientNet-b3, ResNet-50, and MobileNet-V3-small), and an average accuracy of 80.23%.

Cost Analysis of Short Messaging Service and Peer Navigator Interventions for Linking and Retaining Adults Recently Diagnosed With HIV in Care in South Africa.

Large proportions of people living with HIV (PLHIV) in sub-Saharan Africa are not linked to or retained in HIV care. There is a critical need for cost-effective interventions to improve engagement and retention in care and inform optimal allocation of resources. We estimated costs associated with a short message service (SMS) plus peer navigation (SMS+PN) intervention; an SMS-only intervention; and standard of care (SOC), within the I-Care cluster-randomized trial to improve HIV care engagement for recently diagnosed PLHIV. We employed a uniform cost data-collection protocol to quantify resources used and associated costs for each intervention. Compared with SOC, the SMS+PN intervention cost $1284 ($828-$2859) more per additional patient linked to care within 30 days and $1904 ($1158-$5343) more per additional patient retained in care at 12 months, while improving linkage by 24% (95% CI: 11 to 36) and retention by 16% (95% CI: 6 to 26). By contrast, the SMS-only intervention cost $198 ($93-dominated) more per additional patient linked to care and $697 ($171-dominated) more per additional patient retained in care but was not significantly associated with improvements in linkage (12%; 95% CI: -1 to 25) or retention (3%; 95% CI: -7 to 14) compared with SOC. The efficiency of the SMS+PN intervention could be improved by 46%, to $690 more per additional patient linked and $1023 more per additional patient retained in care, if implemented within the Department of Health using more efficient distribution of staff resources. Findings suggest that scale-up of the SMS+PN intervention could benefit patients, improving care and health outcomes while being cost-effective.