Deployment Time Research Articles

Association of deployment characteristics and exposures with persistent ill health among 1990-1991 Gulf War veterans in the VA Million Veteran Program

BackgroundVeterans of the 1990–1991 Gulf War have experienced excess health problems, most prominently the multisymptom condition Gulf War illness (GWI). The Department of Veterans Affairs (VA) Cooperative Studies Program #2006 “Genomics of Gulf War Illness in Veterans” project was established to address important questions concerning pathobiological and genetic aspects of GWI. The current study evaluated patterns of chronic ill health/GWI in the VA Million Veteran Program (MVP) Gulf War veteran cohort in relation to wartime exposures and key features of deployment, 27–30 years after Gulf War service.MethodsMVP participants who served in the 1990–1991 Gulf War completed the MVP Gulf War Era Survey in 2018–2020. Survey responses provided detailed information on veterans’ health, Gulf War exposures, and deployment time periods and locations. Analyses determined associations of three defined GWI/ill health outcomes with Gulf War deployment characteristics and exposures.ResultsThe final cohort included 14,103 veterans; demographic and military characteristics of the sample were similar to the full population of U.S. 1990–1991 Gulf War veterans. Overall, a substantial number of veterans experienced chronic ill health, as indicated by three defined outcomes: 49% reported their health as fair or poor, 31% met Centers for Disease Control and Prevention criteria for severe GWI, and 20% had been diagnosed with GWI by a healthcare provider. Health outcomes varied consistently with veterans’ demographic and military characteristics, and with exposures during deployment. All outcomes were most prevalent among youngest veterans (< 50 years), Army and Marine Corps veterans, enlisted personnel (vs. officers), veterans located in Iraq and/or Kuwait for at least 7 days, and veterans who remained in theater from January/February 1991 through the summer of 1991. In multivariable models, GWI/ill health was most strongly associated with three exposures: chemical/biological warfare agents, taking pyridostigmine bromide pills, and use of skin pesticides.ConclusionsResults from this large cohort indicate that GWI/chronic ill health continues to affect a large proportion of Gulf War veterans in patterns associated with 1990-1991 Gulf War deployment and exposures. Findings establish a foundation for comprehensive evaluation of genetic factors and deployment exposures in relation to GWI risk and pathobiology.

Continuous Integration and Continuous Deployment (CI/CD) Optimization

The advent of Continuous Integration and Continuous Deployment (CI/CD) has fundamentally altered the landscape of software development, enabling teams to deliver updates with unprecedented speed and reliability. By automating the integration of code changes from multiple developers into a central repository, CI/CD practices ensure that software is continuously tested and deployed. This ongoing cycle not only facilitates quicker release cycles but also enhances collaboration among team members and fosters a culture of shared responsibility for code quality. Despite these advancements, organizations face significant challenges in optimizing their CI/CD pipelines. As software systems grow in complexity, the demand for swift and dependable deployments intensifies. This paper explores various techniques and strategies for optimizing CI/CD processes to minimize deployment times while maintaining system reliability. Key optimization methods discussed include: Parallelization of Build Processes: This technique involves breaking down the build process into smaller, independent tasks that can be executed concurrently. By leveraging distributed computing resources, organizations can significantly reduce build times, allowing for faster iterations and deployments. Dependency Caching: Caching dependencies can drastically decrease build times by reusing previously downloaded components. This approach not only speeds up the build process but also minimizes network load and enhances the overall efficiency of the CI/CD pipeline. Incremental Builds: Unlike full builds that compile the entire codebase, incremental builds focus on compiling only the changes made since the last build. This strategy reduces the amount of work needed for each build, accelerating the overall development process. The paper also delves into advanced rollback mechanisms such as blue-green deployments and canary releases. Blue-green deployments allow teams to maintain two identical production environments, enabling smooth transitions and quick rollbacks in case of issues. Canary deployments, on the other hand, introduce new features to a small subset of users before a full rollout, allowing teams to monitor the impact and catch potential failures early. Additionally, automated rollback mechanisms play a vital role in maintaining system reliability, ensuring that any failed deployments can be reverted swiftly to avoid downtime and user disruption. The impact of automation tools on deployment speed and error reduction is another critical aspect examined in this research. Automation frameworks can streamline various stages of the CI/CD process, from code integration to testing and deployment, minimizing human error and ensuring consistent, repeatable processes. By implementing robust automation strategies, organizations can not only accelerate their deployment cycles but also improve overall software quality.

Do Smart Loading Zones help reduce traffic congestion? A causal analysis in Pittsburgh

Rising demand for ride-hailing services and e-commerce delivery intensifies competition for urban curbside spaces, leading to uncoordinated travel behavior, increased traffic congestion and social costs. One possible solution to address those issues is Smart Loading Zones (SLZs), equipped with advanced technologies to optimize curbside use. Yet, the real-world impact of SLZs on traffic flow is unclear due to a lack of real-world data and rigorous studies investigating SLZ’s causal effect on traffic speed. With granular speed data and real-world implementations of SLZs from Pittsburgh, PA, this study applies the regression discontinuity design method to rigorously examine the causal impact of SLZs on traffic speed in the downtown network. The results showed that the introduction of SLZs could enhance the traffic speed of the nearby road segments by 4.5%, while controlling for the underlying trend of speed and multiple influential factors such as time, weather, and road characteristics. In addition, SLZs with a short length could statistically improve traffic speed but those with a long length exert no significant effect. These heterogenous effects might be attributed to the weak enforcement at the time of SLZ deployment in Pittsburgh. The results confirmed the overall positive impact of SLZs on improving congestion. However, policies such as effective dimension planning and robust enforcement policies are essential to maximize the benefits of SLZs.

Improving the Efficiency and Safety of Sentinel Stink Bug Eggs Using X-rays.

Sentinel eggs used to monitor field parasitism of stink bug pests (Hemiptera: Pentatomidae) can only be deployed for a few days to avoid releasing the pest in the monitored area. Using sterile eggs removes the risk of accidental pest introduction and extends deployment time. Freezing the eggs before deployment is one common method of sterilizing sentinel eggs. However, some egg parasitoid species have low or no parasitism on frozen eggs. In this study, X-ray irradiation was used to sterilize Bagrada hilaris sentinel eggs intended for monitoring parasitism by Gryon aetherium (Hymenoptera: Scelionidae), the most promising biological control candidate. In this case, freezing sentinel eggs is not recommended because G. aetherium has low levels of parasitism on frozen eggs. Doses as low as 10 Gy induced 100% sterility. Irradiated eggs successfully sustained the development of G. aetherium and Ooencyrtus californicus (Hymenoptera: Encyrtidae), another egg parasitoid attacking B. hilaris, and parasitism levels were comparable to that of fresh eggs up to seven days old. In addition, G. aetherium showed no preference for fresh non-irradiated eggs over seven-day-old irradiated eggs. Our results indicate that X-ray irradiation is a suitable alternative to produce safe and reliable sentinel eggs to monitor the egg parasitism of B. hilaris and possibly other species.

Towards explainable evaluation: Explaining predicted performance using local performance regions

Identifying which instances in a learning problem are difficult to be predicted by a model is important to avoid critical errors at deployment time as well as to plan how to learn an improved model (e.g., by training data cleaning or augmentation). Previous works have been mainly devoted on measuring instance hardness or developing meta-learners (e.g., assessors) to predict a base model performance based on the instances’ features while neglecting interpretability. In this paper, we propose a method to explain the performance of learned models in a problem based on the induction of meta-rules. Each meta-rule identifies a local region of instances, called Local Performance Region (LPR), where the base model has a predictable performance. The meta-rules are induced using a reduced number of attributes, in such a way that each LPR can be more easily inspected (e.g., by an attribute plot). The proposed method combines assessors, data augmentation and rule induction procedures. Initially, given a dataset of interest and a base model, we build an assessor model, which will be able to predict the base model’s performance for new instances. The assessor is trained based on the test results obtained when the base model is evaluated, thus generalizing the observed errors across instances in the dataset. In our work, we built an assessor in a case study to predict the probability of incorrect classifications of a Random Forest (RF) base model, achieving a mean absolute error of 0.05 in a hold out experiment. Once learned, the assessor is used to predict the model’s errors for new instances in an augmented dataset, covering a variety of features. Finally, meta-rules are learned to approximate the assessor’s predictions in local regions of instances. Experiments show the usefulness of the proposal by finding 18 local regions of bad RF performance, demonstrating a special case of LPRs, called Local Hard Regions (LHRs). By explaining the (in)correctness of model predictions, LPRs constitute a novel application in explainable AI, but focusing on explaining model performance, which can be adopted to different ML contexts.

Advanced test automation techniques for DevOps: Bridging the gap between test-driven development and continuous deployment in agile environments

As software development continues to change, it becomes more important to incorporate modern test automation approaches in DevOps to support efficient deployment. This work investigates TDD and CD in Agile to critically appraise advanced automation technologies and practices to fill existing deficits. This research demonstrates that the brighter Test Automation Framework approaches could improve code quality and deployment time, along with the culture of embracing best practices. The results can help practitioners who want to make their DevOps practices even more efficient and focused on quickly delivering high-quality products.

A seismic-risk-based bi-objective stochastic optimization framework for the pre-disaster allocation of earthquake search and rescue units

Accurately predicting earthquakes' time, location and size is nearly impossible with today’s technology. Severe earthquakes require prompt and effective mobilization of available resources, as the speed of intervention has a direct impact on the number of people rescued alive. This, in turn, calls for a strategic pre-disaster allocation of search and rescue (SAR) units, both teams and equipment, to make the deployment of resources as quick and equitable as possible. In this paper, a seismic risk-based framework is introduced that takes into account distance-based contingencies between cities. This framework is then integrated into a mixed-integer non-linear programming (MINLP) problem for the allocation of SAR units under uncertainty. The two minimization objectives considered are the expected maximum deployment time of different SAR units and the expected mean absolute deviation of the fulfillment rates. We recover the best vulnerability-adjusted routes for each size-location scenario as input to the optimization model using the dynamic programming (DP) approach as part of the broader area of reinforcement learning (RL). The results of the hypothetical example indicate that the comprehensive model is feasible in various risk scenarios and can be used to make allocation-deployment decisions under uncertainty. The results of the sensitivity analysis verify that the model behaves reasonably against changes in selected parameters, namely the number of allowed facilities and weights of individual objectives. Under the assumption that the two objectives are equally important, the model achieves a total deviation of %3.5 from the objectives with an expected maximum dispatch time of 1.1327 hours and an expected mean absolute deviation of 0.01.

Identifying psychological risk and resilience in high-risk military personnel

IntroductionCombat exposure has been associated with the emergence of post-traumatic stress disorder (PTSD) symptoms in military personnel; however, not all veterans are negatively impacted by their operational experience. In many...

Simultaneous measurement of labile U(VI) concentration and (234U/238U) activity ratio using a Monophos®-based Diffusive Gradients in thin-films sampler

BackgroundIn a context of environmental monitoring around installations related to the nuclear fuel cycle, the Diffusive Gradient in Thin-films (DGT) technique captures the integrated concentration of U isotopes in their native environment, yielding comprehensive data on U origin (anthropogenic vs natural), total concentration, and mobility. However, for common deployment times (4–5 days) in moderately basic waters, none of the commercially available binding gels is adapted to measure the total U concentration. So, the development of novel DGT binding gels is timely. ResultsA new DGT sampler, using the Monophos® resin, as well as a new model for the interpretation of the DGT flux, has been successfully developed to measure the labile U concentration (which was also its total concentration) in moderately basic waters (pH ≈ 8). The model accounts for the penetration of uranyl carbonate complexes into the binding gel. Monophos-DGT samplers were able to quantify the total U concentration (accuracy >90 %) in three different mineral basic waters and in a synthetic seawater in laboratory experiments, as well as in situ in the rivers Essonne and Œuf, France. Ion interferences (e.g., Ca2+, Mg2+ and HCO3−), critical when using Chelex and Metsorb resins as binding agents, were overcome by using the new DGT sampler, thus allowing for a longer linear accumulation of U in the tested matrices and, above all, a better detection of U minor isotopes improving the potential of using DGT samplers for water source tracing through isotopic measurements. SignificanceThe use of the new DGT sampler and the new model for the interpretation of DGT flux is recommended to improve the accuracy of total U concentration determinations in field applications. Moreover, simultaneous elemental and isotopic measurements were successfully performed during field application, confirming new perspectives for environmental applications such as identification of U pollution sources by using isotopic signatures.

The Impact of an Automation System Built with Jenkins on the Efficiency of Container-Based System Deployment.

This paper evaluated deployment efficiency by comparing manual deployment with automated deployment through a CI/CD pipeline using Jenkins. This study involved moving from a manual deployment process to an automated system using Jenkins and experimenting with both deployment methods in a real-world environment. The results showed that the automated deployment system significantly reduced the deployment time compared to manual deployment and significantly reduced the error rate. Manual deployment required human intervention at each step, making it time-consuming and prone to mistakes, while automated deployment using Jenkins automated each step to ensure consistency and maximized time efficiency through parallel processing. Automated testing verified the stability of the code before deployment, minimizing errors. This study demonstrates the effectiveness of adopting a CI/CD pipeline and shows that automated systems can provide high efficiency in real-world production environments. It also highlights the importance of security measures to prevent sensitive information leakage during CI/CD, suggesting the use of secrecy management tools and environment variables and limiting access rights. This research will contribute to exploring the applicability of CI/CD pipelines in different environments and, in doing so, validate the universality of automated systems.

On the timing of overt attention deployment: Eye-movement evidence for the Priority Accumulation Framework

On the timing of overt attention deployment: Eye-movement evidence for the Priority Accumulation Framework

Long-term deployment of commonly used DGT devices for trace element measurement across laboratory and field settings

Diffusive Gradients in Thin-films (DGT) technique is a promising passive sampling technique, which was used for the determination of lots of inorganic and organic pollutants. Although many DGT devices have not been extensively tested and verified in field, DGT device such as LSNP-NP, LSNT-NP, LSNZ-NP, and LSNM-NP DGT were widely employed for the assessment of various trace elements. However, the deployment time of these DGTs were much shorter than the theoretical time in the preexisting literature. Therefore, the performance of DGT for long-term application in different water bodies is not known. This investigation utilized the four DGTs for the assessment of various trace elements across extended periods both in controlled laboratory settings and natural field environments. Synthetic soft, hard, and seawater compositions served as media for laboratory deployments. Most elements can be measured accurately in 4–7 days in soft and hard water. Deployment durations in seawater exhibited a notable reduction compared to those in freshwater matrices. LSNZ-NP DGT excelling in oxyanion determination, while LSNT-NP DGT showcased superior efficacy in phosphorus quantification. Field deployments in rivers and sea affirmed the robustness of LSNZ-NP DGT, evidencing prolonged deployment capabilities for various elements, such as Mo, Se, As, Sb, and P, spanning 7–28 days. Assessment of pollution levels across four sampling sites revealed heightened concentrations of most elements in marine waters relative to riverine environments, except for phosphorus. Notably, all assessed elements, except for phosphorus, conformed to Class I water quality standards. This study demonstrated the difference between the theoretical application time and actual application time for the first time. It raises new questions for the application of DGT in nature water bodies. The mechanization of the difference between the theoretical and actual application time should be studied in the future research. The measures to extend the application time should be studied too.

Ambience: an operating system for IoT microservices

Increasingly, the heterogeneity of devices and software that comprise the Internet of Things (IoT) is impeding innovation. IoT deployments amalgamate compute, storage, networking capabilities provisioned at multiple resource scales, from low-cost, resource constrained microcontrollers to resource rich public cloud servers. To support these different resource scales and capabilities, the operating systems (OSs) that manage them have also diverged significantly. Because the OS is the “API” for the hardware, this proliferation is causing a lack of portability across devices and systems, complicating development, deployment, management, and optimization of IoT applications. To address these impediments, we investigate a new, “clean slate” OS design and implementation that hides this heterogeneity via a new set of abstractions specifically for supporting microservices as a universal application programming model in IoT contexts. The operating system, called Ambience, supports IoT applications structured as microservices and facilitates their portability, isolation, and deployment time optimization. We discuss the design and implementation of Ambience, evaluate its performance, and demonstrate its portability using both microbenchmarks and end-to-end IoT deployments. Our results show that Ambience can scale down to 64MHz microcontrollers and up to modern x86_64 servers, while providing similar or better performance than comparable commodity operating systems on the same range of hardware platforms.

Reusable Architecture Growth for Continual Stereo Matching.

The remarkable performance of recent stereo depth estimation models benefits from the successful use of convolutional neural networks to regress dense disparity. Akin to most tasks, this needs gathering training data that covers a number of heterogeneous scenes at deployment time. However, training samples are typically acquired continuously in practical applications, making the capability to learn new scenes continually even more crucial. For this purpose, we propose to perform continual stereo matching where a model is tasked to 1) continually learn new scenes, 2) overcome forgetting previously learned scenes, and 3) continuously predict disparities at inference. We achieve this goal by introducing a Reusable Architecture Growth (RAG) framework. RAG leverages task-specific neural unit search and architecture growth to learn new scenes continually in both supervised and self-supervised manners. It can maintain high reusability during growth by reusing previous units while obtaining good performance. Additionally, we present a Scene Router module to adaptively select the scene-specific architecture path at inference. Comprehensive experiments on numerous datasets show that our framework performs impressively in various weather, road, and city circumstances and surpasses the state-of-the-art methods in more challenging cross-dataset settings. Further experiments also demonstrate the adaptability of our method to unseen scenes, which can facilitate end-to-end stereo architecture learning and practical deployment.

Anchoring device to prevent membrane detachment and preterm prelabor rupture of membranes after fetal intervention.

To assess the feasibility of using a novel device designed for minimally invasive suturing to anchor fetal membranes to the uterine wall and to close surgical defects after fetoscopy. We tested the WestStitch™ suturing device both ex vivo and in vivo. In the ex-vivo studies, 12-Fr trocar defects were created with a fetoscope in five specimens of human uterine tissue with fetal membranes attached. Specimens were examined for integrity of the anchoring stitch. For the in-vivo studies, trocar defects were created in the two uterine horns of three pregnant ewes, each carrying twins at approximately 79-90 days' gestation. One trocar defect in each ewe was repaired using the suture device, and the other was left unrepaired as a control. The repair sites were examined for membrane-anchoring integrity when the defect was created and at delivery. Fetal membranes were anchored successfully to the uterine myometrium using the suture-delivery device in all five experiments performed ex vivo. The in-vivo experiments also revealed successful membrane anchoring compared with controls, both at the time of device deployment and 1-9 weeks after the procedure. We successfully anchored amniotic membranes to the underlying myometrium using a suturing device, both ex vivo and in vivo. Further studies are needed to evaluate the efficacy of the device and to determine whether it can successfully anchor fetal membranes percutaneously in human patients. © 2024 International Society of Ultrasound in Obstetrics and Gynecology.

Passive Seismology: Lightweight and Rapid Detection of Arctic Subsea and Sub‐Aquatic Permafrost

AbstractLow sea levels during the last Ice Age exposed millions of square kilometers of Arctic shelves which have been subsequently submerged, creating subsea permafrost. In onshore settings, permafrost can also exist beneath water bodies such as coastal lagoons, rivers, and thermokarst lakes. We explored passive seismology as a method for mapping unfrozen sediment thickness above subsea and sub‐aquatic permafrost. We present passive seismic data collected with the Mobile Ocean Bottom Seismic Instrument (MOBSI) from the Beaufort Sea near Tuktoyaktuk in Canada, Ivashkina Lagoon on the Bykovsky Peninsula, as well as a lake and river in the Lena Delta, Siberia, Russia. We use borehole data and frost probe measurements to identify permafrost‐related H/V measurement peaks and calibrate shear wave velocities for frequency‐to‐depth conversion. We employ the shortest path and maximum signal amplitude to connect peaks and generate geological profiles. The MOBSI detected the ice‐bonded permafrost table beneath the Beaufort Sea, as well as beneath a Siberian lake and lagoon. At Tuktoyaktuk, an ocean bottom seismometer revealed a 5% scatter about the peak frequency for three‐minute time windows and over 8 hr of recording time. With peak frequencies ranging from 4.9 ± 0.2 Hz to 27.6 ± 1.4 Hz, the depth to subsea permafrost ranged from 1.4 ± 0.1 m bsl at the shoreline to 14.0 ± 0.4 m bsl 240 m offshore. Given an accurate shear wave velocity, our findings highlight that MOBSI deployment times as short as 3 min are adequate for detecting Arctic subsea and sub‐aquatic permafrost.

Brivaracetam use in children with epilepsy: A retrospective multicenter study

PurposeThis retrospective multicenter study aimed to assess the efficacy and safety of brivaracetam (BRV) in pediatric epilepsy. MethodsOur cohort consisted of 93 children (mean age 11.5 ± 7.5 years) with a wide spectrum of pediatric epilepsy, including epileptic encephalopathy and generalized epilepsy. Of these, 61 (60.4%) were diagnosed with focal epilepsy, 19 (15.8%) with generalized epilepsy, and 16 (15.8%) with combined epilepsy, while 8 patients (7.9%) had an unknown epilepsy type. The cohort included rare epilepsy syndromes: 8 patients with Lennox-Gastaut syndrome, 3 with Dravet syndrome, and 1 with Rasmussen syndrome. Patients had a history of various antiseizure medications (ASMs) (6.42 ± 3.15), and on average, were being treated with more than two (2.57 ± 1.16) drugs at the time of BRV deployment. ResultsRetention rates were high, with 80.6% of patients adhering to treatment at 3 months, 66.7% at 6 months, and 45.2% at 12 months. In 29 patients (30.1%), BRV was added in an overnight switch from levetiracetam (LEV), resulting in a reduction of behavioral adverse effects (AEs) in 5 patients (17.2%). The response rate was 25.8% at 3 months, 16.1% at 6 months, and 17.2% at 12 months, with no responders in the epileptic encephalopathy group. Therapy tolerance was notable, with 70 patients (75.3%) reporting no AEs. Transient AEs occurred in 10 patients (10.7%), and in 13 cases (14.0%), the AEs warranted dose adjustment or discontinuation of BRV. ConclusionApproximately one-fifth of pediatric patients with drug-resistant epilepsy responded to BRV, with the best response observed in patients with focal seizures. However, the impact on patients with epileptic encephalopathy was limited.

Feral cat control: improving Eradicat® bait efficiency and effectiveness for fauna conservation in the Southern Jarrah Forest, Western Australia

Context Toxic meat baits are the most effective broadscale method used for reducing the densities and impacts of feral cats (Felis catus) on vulnerable Australian native fauna when alternative prey is minimal. Aims Our aim was to assess the efficiency (proportion of baits removed by target animals) and effectiveness (proportion of target animals removed) of Eradicat® baits and to identify how their use may be improved in Southern Jarrah Forest ecosystems of Western Australia. We sought to determine how, when, and where best to deploy baits using the current Eradicat® bait prescriptions to maximise the reduction of feral cats. Methods Eradicat® uptake trials were conducted over a 15-month period using remote sensor cameras (RSCs) to observe animals interacting with the baits at 40 sites. Ten successive baiting trials were conducted, each involving four randomly selected sites (two replicates for each of two bait deployment methods: clusters and transects). Key results The fate of 5658 Eradicat® baits at 2000 bait locations was recorded during 54,361 camera trap nights. Despite occupancy rates being high for cat and fox (Vulpes vulpes), (92% and 84%, respectively), the efficiency and effectiveness of Eradicat® baits was low for both introduced predators (cat: 0.1% and 10–12%, respectively; and fox: &lt;0.6% and 8–20%, respectively). There were no major differences in baiting efficiency in relation to bait deployment method or time of year. More than half (56–58%) of the baits were removed by non-target animals prior to an introduced predator being observed on camera at the bait location. Along transects, there were more cat and fox visits closer to tracks and surface water features. Foxes were also more likely to visit bait locations closer to private property. Younger cats appeared to be more interested and more likely to eat a bait than older cats. Conclusions Substantial improvements could be made by increasing bait availability (reducing non-target interference), detectability and attractiveness to cats. Implications Additional introduced predator threat abatement methods may be needed for the conservation and recovery of many threatened native mammals in the Southern Jarrah Forests and elsewhere in Australia. Feral cat baiting should be conducted within an integrated and holistic invasive animal management system.

Exposome Signatures of Gastrointestinal Disorders Among Active Duty Service Members.

We evaluated the risk factors associated with gastrointestinal disorders (GD) among the soldiers of the Army and Marine Aviation community (AMAC) using an exposomic approach. Specifically, we aimed to determine the medical and operational factors associated with reported GD in the Military Health System. Longitudinal data were obtained from the Medical Assessment and Readiness System (MARS) housed at Womack Army Medical Center, Fort Liberty, NC, for a retrospective cohort study that included 79,249 active duty United States AMAC active duty service members (ADSMs) from October 2015 to December 2019. Multivariable mixed-effects logistic regression was used to assess the relationship between the incidence occurrence of GD and variables including rank, service time, deployment, Armed Forces Qualification Test score, education, tobacco use, alcohol use, age, gender, race, ethnicity, body mass index (BMI), and marital status. The incidence of GD included 22,813 person-years of observations with a rate of 2.2 per 100 person-years and a period prevalence of 3.2%. GD was independently associated with rank, service time, and deployment (all P < 0.05). The high-risk categories associated with GD in the AMAC included age, BMI, race (not ethnicity), marital status, and service time. Our data also show that deployment time, not geographical location, was associated with a higher risk of GD. Together, these analyses suggest that White ADSMs older than 33 years of age who have experienced marriage, longer service time, and deployments appear to have a higher risk of GDs. Our assessment shows the utility of using an exposomic approach to create a member-specific, big data-informed personalized clinical algorithm of health outcomes.

Optimization of node deployment in underwater internet of things using novel adaptive long short‐term memory‐based egret swarm optimization algorithm

SummaryOptimizing node deployment in the underwater Internet of Things (UIoT) poses significant challenges due to the complex and dynamic nature of underwater environments. This research introduces the adaptive long short‐term memory‐based egret swarm optimization algorithm (ALSTM‐ESOA), a novel approach designed to enhance network coverage and performance efficiently. Unlike traditional methods, ALSTM‐ESOA incorporates cognitive learning capabilities from long short‐term memory (LSTM) and dynamic adaptation strategies inspired by the hunting behaviors of egrets. The algorithm's effectiveness was tested through extensive simulations in MATLAB, demonstrating notable improvements over existing models: network throughput increased by up to 55.56%, deployment time decreased by 88.89%, and energy efficiency improved significantly. These enhancements are critical for robust, real‐time data collection and monitoring in underwater settings, providing substantial benefits for marine research and resource management. The findings suggest that ALSTM‐ESOA significantly outperforms conventional algorithms, offering a promising new tool for the advancement of UIoT applications. After being implemented in MATLAB, the suggested ALSTM‐ESOA model for the node deployment optimization in UIoT is examined. The proposed ALSTM‐ESOA in terms of network throughput is 55.56%, 38.89%, 36.11%, and 11.11% better than CNN, LSTM, ARO‐RTP, and IGOR‐TSA, respectively. Similarly, the proposed ALSTM‐ESOA with respect to deployment time is 88.89%, 81.82%, 75%, and 50% better than CNN, LSTM, ARO‐RTP, and IGOR‐TSA, respectively. For the purpose of exploring marine resources, monitoring underwater environments, and conducting marine scientific investigation, the research's findings are extremely valuable.