Operational Factors Research Articles

The Canadian Prospective Pragmatic Perilunate Outcomes (C3PO) trial; a protocol

BackgroundPerilunate spectrum injuries (PSI) are uncommon, but high-energy injuries that result in significant functional implications for patients. The existing literature on PSI is limited to small case-series and there are no evidence-based guidelines for management.MethodsThis manuscript outlines a protocol for a three-armed, national, multi-centre study, which includes a patient injury registry for PSI, as well as associated retrospective and prospective arms. The primary objective is to establish a national injury registry for patients who present with PSI. The secondary objectives are to examine the long-term clinical and radiographic outcomes of PSI, evaluate progression of these outcomes over time, and to examine the impact of specific operative and injury related factors on outcomes.DiscussionThis paper describes the protocol for a three-armed, multi-centre study of PSI. It outlines the rationale for the study as well as the relevant methodological details. An injury registry involving multiple centres is valuable given this is an uncommon injury with many potential variables affecting outcomes. Registry-based retrospective and prospective studies will benefit from the large clinical database provided by the registry and could be used to guide evidence-based treatment guidelines which are currently lacking in the literature.Trial registrationClinicaltrials.gov identifier: NCT04370626.

Impact of Leaching Operational Factors on Nickel and Manganese Recovery from Black Masses of Spent Lithium-Ion and Nickel-Metal Hydride Batteries

Impact of Leaching Operational Factors on Nickel and Manganese Recovery from Black Masses of Spent Lithium-Ion and Nickel-Metal Hydride Batteries

Stressors and Support Experienced by Active Duty U.S. Navy Sailors at Sea.

High occupational stress is generally associated with poorer mental health and reduced performance in military personnel, whereas access to support resources is associated with positive outcomes. However, little research has examined the unique stressors and supports experienced by sailors on ships while underway at sea. The objectives of this study were to (1) identify sailors' underway stressors and supports and (2) examine how these differ as a function of demographic and operational factors. Active duty U.S. Navy sailors reporting an underway period lasting at least 30 days within the last 5 years (n = 441; 74.7% male, 90% enlisted) completed an anonymous retrospective survey. Sailors completed measures assessing underway stressors (25 items) and supports (9 items), as well as demographic, military, and operational characteristics (sex, rank, ship type, and underway duration). The study protocol was approved by the Naval Health Research Center Institutional Review Board in compliance with all applicable federal regulations governing the protection of human subjects. Overall, the stressors rated as most problematic were exposure to loud noises, inability to rest when needed, unavailability of quality mental and physical health care, and not having enough people to complete the mission. Sailors perceived the most available supports to be opportunities to socialize, safety while performing duties, and finding ways to relieve stress. Female and enlisted personnel, relative to male and officer personnel, respectively, generally perceived stressors as more problematic and support resources as less available. Nearly half of the sample (48.3%) reported that services and programs to help them deal with stress while at sea were inadequate. Sailors experience many stressors while underway. Increasing the resources available to underway sailors may be warranted, especially for female and enlisted personnel. Additional research is necessary to develop targeted interventions and resources. Possible recommendations include noise mitigation, berthing area improvements, sleep and/or scheduling interventions, improved access to mental and physical health care, and interventions to reduce the stigma associated with seeking mental health care.

Workflow optimization for mobile computing

Mobile workflow optimization has emerged as a critical factor in modern business operations, transforming how organizations manage and execute their processes across distributed environments. This comprehensive article explores the fundamental components, implementation strategies, and future directions of mobile workflow optimization. The article examines cloud integration, artificial intelligence implementation, security frameworks, and technical considerations that drive successful mobile workflow deployments. Through detailed analysis of real-world implementations, the research demonstrates how organizations improve operational efficiency, user satisfaction, and resource utilization while maintaining robust security measures. The investigation encompasses development methodologies, monitoring strategies, and emerging technologies reshaping the mobile workflow landscape, providing insights into best practices and future considerations for organizations pursuing the mobile-first operational strategy.

Best practice portals in health promotion and disease prevention: approaches, definitions, and intervention evaluation criteria

IntroductionThe evaluation of practices is a valuable source of evidence in the context of an evidence-based approach to public health. Best practice portals (BPPs) are promising tools for facilitating access to recommended programmes, monitoring and improving the quality of interventions. There are several such portals in Europe, but there is little work in the scientific literature on the subject. The study aimed to identify and characterise BPPs in health promotion and disease prevention and analyse the approaches, definitions, and criteria for evaluating interventions.MethodsTo identify portals, websites of public health institutions and organisations, the PubMed database and grey literature were searched. The material consisted of elements of each portal’s design, information available on their websites, and collected publications. The study applied a qualitative analysis with a descriptive approach and covered a detailed description of the four selected portals.ResultsAmong the analysed BPPs, three were from the European region, and one was from Canada (pioneer in developing best practice tools). The dates of launching the portals ranged from the year 2003 to 2016. The number of interventions collected in the databases ranged from 120 to 337. Portals were useful, well-designed, and developed tools. BPPs differed in terms of their objectives and roles, adopted standards and criteria for assessing practices, and other operational factors. In each portal, interventions underwent a rigorous and multilevel assessment process conducted by independent experts in the field and based on intervention evaluation criteria. Generally, the analysed catalogues described similar issues, e.g., Selection of the issue addressed by the practice, Description of a particular element of the practice, Theoretical foundation, or Evaluation/Effectiveness. However, we identified both similarities and differences in the adopted terms (names of criteria) and their definitions. It was shown that sometimes the same criterion had different names depending on the catalogue. On the other hand, criteria with identical or similar names could be defined differently within the detailed thematic scope.ConclusionThe similarities and differences presented in this work can serve as a valuable starting point for designing such tools to support practice-based and evidence-based decision-making in health promotion and disease prevention.

Optimizing Nanobubble Production in Ceramic Membranes: Effects of Pore Size, Surface Hydrophobicity, and Flow Conditions on Bubble Characteristics and Oxygenation.

Precise control of nanobubble size is essential for optimizing the efficiency and performance of nanobubble applications across diverse fields, such as agriculture, water treatment, and medicine. Producing fine bubbles, including nanobubbles, is commonly achieved by purging gas through porous media, such as ceramic or polymer membranes. Many operational factors and membrane properties can significantly influence nanobubble production and characteristics. This study examines how membrane pore size, surface hydrophobicity, and gas/water flow conditions affect nanobubble size and concentration. Findings reveal that reducing the ceramic membrane pore size from 200 to 10 nm slightly decreased the mean nanobubble diameter from 115 to 89 nm. Furthermore, membranes with a hydrophilic outer surface and hydrophobic pore surface generated smaller nanobubbles with higher concentrations in water. Additionally, a high water cross-flow rate (e.g., >1 L·min-1) increased the nanobubble concentration, though bubble size remained unaffected. In contrast, the gas flow rate had a more pronounced effect. Increasing the gas flow rate from 0.5 to 12 L·min-1 significantly raised the nanobubble concentration from 3.09 × 108 to 1.24 × 109 bubbles·mL-1 while reducing the mean bubble diameter from 100 to 79 nm. An interfacial force model was applied to analyze bubble detachment at the membrane pore outlet, considering factors such as gas flow/pressure, surface tension, and shear forces from the water flow. These findings offer valuable insights into the mechanisms governing nanobubble generation via gas injection through porous membranes.

Decentralized Public Transport Management System Based on Blockchain Technology

The development of intelligent transportation systems (ITSs) is penetrating many economies around the globe. This paper presents three key innovations in the field of intelligent transportation systems, as follows: (1) a novel tokenization approach where each vehicle is represented as a macro-token subdivided into 500,000 micro-tokens for precise condition monitoring, (2) a comprehensive mathematical model for vehicle state assessment incorporating multiple operational factors, and (3) the GDEPZ method for optimizing data transmission via satellite communication. These innovations enable the autonomous control of technical conditions, transparent fleet management, and efficient data processing in hard-to-reach areas. Various researchers in both industry and academia are looking into more efficient management methods for both vehicles and related data processing aspects. A vast trend related to the latter is the distributed data processing of transmitted data. This article discusses approaches to the use of blockchain technology in ITSs. It explores the use of blockchains in modern transport industries. In particular, the paper proposes a novel approach to the maintenance of public transportation vehicles and buses. The specificity of the proposed approach is the autonomous control of technical conditions using information systems. When using blockchain technology, building a transparent vehicle fleet management system is possible. The specificity of the proposed approach lies in data processing. Within the organization, confidence in data increases, the possibility of manipulating transportation is eliminated, and the decision-making chain is reduced. As a result, the system can manage itself. This also helps to increase the service life of vehicles, makes it possible to predict their malfunctions, and improves the quality of data on their technical conditions.

A strategic perspective on automated warehouse systems in retail: insights from a multiple case study

PurposeAs the warehouse is increasingly viewed as a strategic component, retailers’ willingness to invest in automated warehouse systems (AWS) has increased. These investment decisions are influenced by well-known operational factors, but strategic factors, which have received limited attention in warehousing literature, also play a pivotal role. Addressing this gap, this study investigates how strategic factors influence AWS investment decisions in retail.Design/methodology/approachBased on a theoretical foundation of technology adoption, strategic intent, and automation strategy, an abductive multiple case study is conducted with eight purposefully selected retailers that had implemented or were in the process of implementing a large AWS.FindingsThe study ranks 10 competitive priorities and 21 AWS evaluation aspects and shows how the firm’s strategic intent and the AWS investment decisions can be connected via the formulation of a warehouse automation strategy. The findings reveal the content for such a strategy – including 7 categories and 17 considerations – related to, for example, technology innovativeness, efficiency versus adaptiveness, technology-supplier relationships, control and ownership, and risk exposure. The study empirically shows how manager characteristics and owner strategies influence retailers’ AWS investment decisions. Four strategic intent profiles are abductively developed: reliability and delivery service; profitable deliveries; scalable logistics for volume growth; and platform building for logistics services. The study also provides evidence of a reciprocal relationship between strategic intent and AWS investment decisions.Research limitations/implicationsThe study is conducted with a limited number of Swedish retailers, indicating a need for additional studies to test the findings across different contexts.Practical implicationsThe study offers a framework for formulating a warehouse automation strategy. As a foundation for developing the framework, the study shares empirical insights from retailers in the forefront of AWS implementation.Originality/valueThe study contributes as a conversation changer by showing the importance of shifting from a tactical-operational focus to a strategic perspective on warehouse configuration in general and on AWS investment decisions in retail in particular.

Predictive analysis of fuel oil consumption in vessels: interpretable modelling with emphasis on load conditions

ABSTRACT Fuel oil consumption (FOC) in vessels is influenced by various factors, with vessel load conditions being a critical determinant. This research aimed to develop an interpretable regression-based machine learning model, specifically an XGBoost Regressor, to predict FOC, and leveraged the analysis with Explainable Artificial Intelligence (XAI) techniques to enhance transparency and understanding of the factors affecting FOC in maritime operations. Hyperparameter tuning optimized model parameters, achieving a high R-squared (R2) value of 0.99. XAI methods clarified how operational (e.g., speed, load, draft) and environmental factors (e.g., wind, wave, current, sea state) contribute to FOC increases. As a result, operational factors, notably average draft, exerted a substantial influence, with an average draft of 18.425 m resulting in a significant increase in predicted FOC to 1,729.40 kg/h during the laden condition, compared to the base value of 1,496.82 kg/h. Additionally, environmental factors, particularly Relative Wind Angle, significantly impacted FOC prediction, with a Mean Absolute SHAP Value of 11.97 kg/h, notably higher than in ballast (2.59 kg/h) and empty (2.85 kg/h) conditions. These findings emphasize the importance of tailored fuel efficiency strategies, as operational and environmental factors may vary in their impact across different loading conditions. ‘This article is a revised and expanded version of a paper entitled “Predictive Analysis of Fuel Oil Consumption in Vessels: Interpretable Modeling with Emphasis on Load Conditions” presented at The 11th International Conference on Logistics and Maritime Systems (LOGMS 2023) on 6 September 2023 at the Busan Port International Exhibition & Convention Center (BPEX)’

A Structural Equation Model For Adopting Additive Manufacturing in the Footwear Firms Supply Chains

Goal: The objective of this research is to present a theoretical framework and explore how additive manufacturing (AM) techniques affect supply chain complexity (SCC) in the footwear sector. Design/Methodology/Approach: This study developed theoretical framework that includes AM best practices and SCC through extensive literature review. Using 1-5 likert scale surveys, data were gathered from 205 professionals working in 29 Ethiopian footwear industries in the period October 20 to December 23, 2023. The collected questionnaires were tested for reliability and validity, measurement and structural model fit test were checked using confirmatory factor analysis. Structural Equation Modeling using AMOS v23 was used to evaluate the proposed correlations. Results: The confirmatory factor analysis test result revealeld that measurement and structural equation model fit test fulfill the model fit test requirements, i.e. χ2/df < 5, CFI, GFI and TLI > 0.9, RMR and RMSEA < 0.08. The findigs of the study confirmed that additive manufacturing best practices (time, inventory, operation, and resource, energy and waste related factors) have positive effects on static and dynamic supply chain complexity. Practical implications: This study helps the firm to focus on adoptation of AM for improving supply chain complexity. Furthermore, this study extended earlier research in the domains of SCM by building a theoretical framework that connects AM best practices with supply chain complexity factors. Originality/value: This work bridges the scientific knowledge gap by combining supply chain complexity and AM best practices. Among others, it can contribute to the existing literature by illustrating the benefits of adopting AM technology particularly in footwear sector.

Sustainable Emulsified Acid Treatments for Enhanced Oil Recovery in Injection Wells: A Case Study in the Qusahwira Field

Emulsified acid treatments present an innovative and environmentally sustainable alternative to conventional hydrochloric acid (HCl) methods in enhancing oil recovery. This study investigates the application of a stable emulsified acid formulation in matrix acidizing operations to improve injectivity in four wells within the Qusahwira Field. Compared to traditional 15% HCl treatments, the emulsified acid demonstrates deeper acid penetration and retardation effect leading to enhanced injection rate. By delivering deep worm holing effects against calcium carbonate formation, this dual-phase system enhances injectivity by 14 times while minimizing the environmental and material impacts associated with spent acid volumes. The methodology integrates advanced neural network modeling to predict stimulation outcomes based on 15 operational and reservoir factors. This model reduces the trial-and-error approach, cutting operational costs and time for carbonate reservoir. Field trials reveal significant improvements in injection pressure and a marked reduction in circulation pressure during stimulation, underscoring the treatment’s efficiency. Developed in a Superior Abu Dhabi laboratory, the emulsified acid achieves high-temperature stability (200 °F) and deep acid penetration, further reducing the ecological footprint of acid stimulation by enhancing operational precision and reducing chemical use. This paper highlights a sustainable approach to optimizing reservoir productivity, aligning with global efforts to minimize environmental impacts in oil recovery processes.

Transient fluctuation prediction and control of tracked vehicle electric drive engine-generator set based on LSSVM

To address the challenges of delayed control responses and suboptimal performance due to the absence of predictive capabilities for pre-power chain speed fluctuations in the electromechanical composite transmission system of armored vehicles, a transient fluctuation prediction and control method based on the Least Squares Support Vector Machine (LSSVM) is proposed for the engine-generator set within the system. This approach leverages real-world generator data collected from actual vehicles as the training dataset to establish a data-driven model. A specific LSSVM training model is developed, with experimental data serving as the test set. Within the model's predictive framework, transient fluctuations of critical engine-generator parameters are generated in real-time under test conditions. Simulations are conducted on a test platform for the electromechanical composite transmission system, evaluating both single-generator operation and a variety of driving conditions. Comparative analysis is performed to assess the operational factors influencing system performance under single and multiple conditions, as well as the control effects of transient power chain fluctuation prediction. Under multiple-condition scenarios, the system demonstrates faster dynamic recovery in response to significant load disturbances, with voltage peak fluctuations remaining within 5 %, which meets engineering application standards. This validates the model's adaptability and generalization capability for broader use cases.

Predicting 90-day return to the emergency department in orthopaedic trauma patients in the Southeastern USA: a machine-learning approach

IntroductionReturn-to-acute-care metrics, such as early emergency department (ED) visits, are key indicators of healthcare quality, with ED returns following surgery often considered avoidable and costly events. Proactively identifying patients at...

Utilization of AF4 for characterizing complex nanomaterial drug products: Reexamining sample recovery and its impact on particle size distribution as a quality attribute.

Asymmetrical flow field-flow fractionation (AF4) with multi-detection has continued to gain wider acceptance for characterizing complex drug products. An important quality attribute for these products is the measurement of the particle size distribution (PSD). Current limitations of established procedures (e.g., dynamic light scattering) for accurately determining PSD can be overcome by AF4. However, while gaining acceptance this technique has not been fully adopted within the pharmaceutical industry. A technical understanding of fundamental operational factors is necessary for the successful application of utilizing any emerging technology. For example, recovery (R% = AS/AD*100, where AS and AD are the peak areas from the concentration detector with and without the crossflow field, respectively) is one factor that is used to assess the robustness during AF4 method development, but currently little is known about the interplay between analyte recovery and PSD. This work highlights factors that impact calculated AF4 recovery, and how differences in analyte and absolute recovery ultimately influence the PSD of nanoparticle size standards and complex drug product formulations such as emulsions and liposomes. Factors like ionic strength, buffer composition, and analyte chemistries, which are the most common factors associated with changes to R% in AF4, contributed to changes in AS. While AD is not typically examined in detail, the selection of the concentration detector (UV or dRI) along with their instrumental parameters (e.g., wavelength, attenuation value, linear range) and sample preparation was shown to under- or over-estimate AD thus changing R%. Examining both components of R% and their contributions to analyte and absolute recovery show that decreases in analyte recovery may not be exclusively due to sample loss but could be influenced by changes in analyte-membrane interactions or analyte instability. Because of this, four relationships between recovery and PSD were defined. While R% is used as a tool for assessing AF4 methodology, the factors investigated through this work warrant further considerations when establishing an appropriate R% threshold.

Impact of Operational and Financial Efficiency on Aviation Stock Prices: A Machine Learning Model with SHAP Interpretability

Using a machine learning approach, this study examines how operational and financial efficiency metrics influence stock prices in the aviation industry. A CatBoost regression model enhanced with SHapley Additive exPlanations (SHAP) was developed using data from 65 global aviation companies collected between 2015 and 2023. The model predicts stock prices based on various operational and financial indicators, including Total Revenue per Available Seat Mile (ASM), Passenger Load Factor, liquidity ratios, and debt-to-assets ratios. The findings suggest that operational efficiency metrics, particularly Total Revenue per ASM and Passenger Load Factor, play a significant role in predicting stock prices within the aviation sector. Financial metrics, such as the Quick Ratio and Debt-to-Assets Ratio, also contribute to the model but appear to have a secondary influence compared to operational factors. SHAP values provided interpretable insights into the model's predictions, allowing for a better understanding of the relative importance of different features. Furthermore, the study's findings offer support for the semi-strong form of the Efficient Market Hypothesis (EMH), demonstrating that operational and financial metrics are reflected in stock prices. These results indicate that aviation companies demonstrating higher operational efficiency may be better positioned for favorable stock market performance, although financial health remains important. This study contributes to the existing literature by integrating operational and financial metrics into a machine learning framework, offering a comprehensive and interpretable model for stock price prediction in the aviation industry.

Influencing Factors and Purification Performance of a Negative Ion Air Purifier for Indoor Ammonia Gas Removal

The negative ion air purifier (NIAP) has been used for capturing particulate matter. Nevertheless, the knowledge on its effectiveness in removing other air pollutants such as ammonia gas remains limited. In this study, the effect of an NIAP for indoor ammonia gas removal was evaluated through a series of experimental studies. The applicability and different influencing, operating, and environmental factors on the ammonia gas removal performance were firstly investigated by conducting a series of experiments. Then, in order to understand the performance of the NIAP and the spatial distribution of ammonia gas and other by-products, indoor field measurements of ammonia gas, ozone, and negative ion concentrations in a real bathroom were performed for different cases with the NIAP turned on and off. The results indicated that negative ions were effective in reducing ammonia gas concentration. The operating and environmental factors including upstream wind speed, degree of operating voltage, and initial ammonia gas concentration have great influences on the ammonia gas removal efficiency of the NIAP. The highest removal efficiency can reach to 95.8%, when the upstream wind speed was 0.8 m/s and the degree of operating voltage was at gear 3 (3.0 kV). The purification efficiency of ammonia gas for the NIAP could reach up to 80%.

Combined UV/H2O2 and biochar processes for enhanced removal of contaminants of emerging concern in dry wells.

Dry wells are neighborhood-scale stormwater infiltration systems increasingly used in drought-prone areas for stormwater capture and groundwater recharge. These systems bypass the low permeability surface soil to maximize infiltration rates. However, hydrophilic contaminants of emerging concern (CECs) in urban runoff pose potential groundwater contamination risks. Field monitoring in this study confirmed the presence of CECs and the inability of current dry wells to remove these compounds. To address this, we explored stormwater treatment systems that: (1) can be easily operated in dry wells; (2) effectively remove hydrophilic CECs under realistic infiltration rates; and (3) offer multi-year field lifespan. Batch isotherm and kinetic studies were conducted on a large-grain biochar (d50 = 2.24 mm) to assess removal efficiency for seven hydrophilic CECs (logKow ≤ 4). Two column experiments evaluated biochar filters (5 wt%) with and without UV/H2O2 pre-treatment under continuous high-throughput conditions. Results showed that biochar filters effectively removed five of seven CECs, with reduced efficiency for anionic compounds. The UV/H2O2 process with approximately 1500 mJ/cm2 UV dose degraded all CECs by >50 %, except dicamba, with direct photolysis as the dominant mechanism due to strong hydroxyl radical scavenging by dissolved organic carbon (DOC). Combining UV and biochar reduced CEC breakthrough concentration by 4 - 92 %. Based on the results, a contaminant transport model was calibrated and used to estimate the system lifespan in dry wells in Los Angeles. Findings showed that the insecticide, imidacloprid, is the limiting contaminant for the system's lifespan. Without UV/H₂O₂ pre-treatment, the system could last about four years with 10 wt% biochar in the filter. With pre-treatment, the lifespan will be determined by other operational factors rather than CEC removal. This study developed effective stormwater treatment systems for dry wells, supporting future local stormwater capture projects while safeguarding groundwater quality.

Risk Factors for Recurrent Instability After Arthroscopic Bankart Repair in Pediatric and Adolescent Patients: A Systematic Review.

While risk factors for recurrent instability (RI) after arthroscopic Bankart repair (ABR) for anterior glenohumeral instability (aGHI) have been well established in adult populations, there is much less evidence in pediatric and adolescent patients, despite being the most affected epidemiologic subpopulation. To identify the clinical, demographic, radiologic, and operative risk factors for RI after ABR for aGHI in pediatric and adolescent patients. Systematic review; Level of evidence, 4. This systematic review was performed according to the PRISMA guidelines (Preferred Reporting Items for Systematic Reviews and Meta-Analyses). Three databases (PubMed, Embase, and Ovid [MEDLINE]) were searched from data inception to July 5, 2023, using the terms "pediatric,""shoulder instability," and "Bankart repair," allowing data on patients with age up to 19 years. The Methodological Index for Non-randomized Studies tool was used to assess the quality of included studies. Eleven studies met the criteria, with 767 patients (770 shoulders) with a mean age of 16.4 years (range, 12-19; 18% female). The pooled RI rate was 28%, and the revision aGHI surgery rate was 20%. The following risk factors were identified for RI after ABR, in descending order of frequency of identification, in terms of number of studies: younger age and greater glenoid bone loss, as well as the presence and/or greater size of a Hill-Sachs lesion (HSL) (2 studies each), followed by off-track HSL, >1 dislocation before index surgery, overhead and/or contact sports participation, larger anterior labral tear size, loss of glenoid retroversion, greater number of anchors used, lower postoperative patient-reported outcome scores (PROs), nonunion after bony Bankart repair, and absence of concomitant remplissage (1 study each). In pediatric and adolescent patients with aGHI, the most common risk factors identified for RI after ABR were younger age, greater glenoid bone loss, and the presence and greater size of an HSL. Other risk factors included >1 dislocation before index surgery, participation in overhead and/or contact sports, larger anterior labral tear size, loss of glenoid retroversion, greater number of anchors used, lower postoperative PROs, nonunion after bony Bankart repair, and absence of concomitant remplissage.

Stock Market Price Forecasting Using Metaheuristic Search Algorithms: A Comparative Analysis

Stock market forecasting is an essential factor in the daily operations of many companies and individuals. However, the complex and nonlinear nature of the stock market and the unpredictable variations in factors affecting stock prices present significant challenges in accurate forecasting. To address this, we employ four model-based metaheuristic search algorithms (MHs), namely the Crow Search Algorithm (CSA), Particle Swarm Optimizer (PSO), Gray Wolf Optimizer (GWO), and Dandelion Optimizer (DO), to estimate the parameters of stock market prices models. The data utilized in our experiments are extracted from the widely recognized stock index of Standard & Poor’s 500 (S&P 500), that serves as a representative benchmark for the United States stock market. Our findings demonstrate that the CSA outperforms other MHs by providing the best combination of parameters for modeling stock market prices. The optimized parameters for the CSA model yielded Variance-Account-For (VAF) values of 97.846% in the training set and 93.483% in the testing set. This suggests that CSA offers promising capabilities for enhancing the accuracy and effectiveness of stock market forecasting models.

Trends and Factors Associated with Insomnia and Sleep Apnea in U.S. Military Aviators, 2006-2022.

Insomnia and sleep apnea (SA) can have adverse effects on operating aircraft. This study examined trends in insomnia and SA incidence rates in U.S. military aviators from 2006-2022 and examined associated demographic and operational factors. Data on incidence cases of outpatient insomnia and SA, aviator demographics, and operational factors were obtained from the Defense Medical Epidemiological Database, a comprehensive online archive of military medical data. Insomnia and SA cases were identified using specific International Classification of Diseases codes. For the entire military aviator population (N = 331,992 ± 1649, mean ± SD) overall rates of insomnia and SA were 10.2 and 24.9 cases/1000 person-years, respectively. Compared to the entire U.S. military nonaviator population, aviators had lower incidence of insomnia, but higher incidence of SA. Insomnia rates among aviators increased up to 2015 and declined afterwards, while SA rate generally rose over the period. Insomnia and SA rates among aviators increased with age, were higher among those of black and other nonwhite races, were highest among Army personnel (compared to other services) and were highest among helicopter pilots (compared to fixed-wing aircraft pilots). Insomnia rates were similar among male and female aviators, but SA rates were higher among men. These data indicate that insomnia and SA are prevalent within the military aviation population; however, the incidence of insomnia appears to have fallen as the tempo of military operations has decreased while the incidence of SA continues on an upward trajectory, possibly associated with the growing problem of obesity. Caldwell JA, Knapik JJ. Trends and factors associated with insomnia and sleep apnea in U.S. military aviators, 2006-2022. Aerosp Med Hum Perform. 2025; 96(1):23-30.