Adaptive Design Research Articles

Characterization of medical device randomized controlled trials with adaptive designs.

Aim: Adaptive designs are frequently used in drug randomized controlled trials (RCTs). However, their use in medical device RCTs remains unclear. We aimed to characterize medical device RCTs with adaptive designs. Materials & methods: We searched for adaptive RCTs in the following databases: ClinicalTrials.gov, International Clinical Trials Registry Platform and the International Standard Randomised Controlled Trial Number registry. Adaptive design keywords and medical device corporation names were used as terms to search the trial records registered between 1 January 2000 and 18 October 2024 in the databases. The annual number and proportions of adaptive trials were analyzed, and characteristics such as design type, sponsor, therapeutic area, trial stage and regulatory status were summarized. Results: Overall, 105 adaptive RCTs were identified from ClinicalTrials.gov, accounting for 2.112 per 1000 trials in 49,721 medical device clinical trials registered in ClinicalTrials.gov during the period. The average annual number of adaptive RCTs per 1000 clinical trials was the highest (8.55±11.65) during 2005-2010, reduced to 3.33±2.35 during 2011-2016, and significantly decreased to 1.29±0.85 during 2017-2024 (p = 0.011). The most common adaptive designs were group sequential design (GSD, 50.5%), sample size reassessment (SSR, 17.1%) and investigating both superiority and non-inferiority (10.5%). Most RCTs were sponsored by the private sector (62.9%), conducted in Europe/North America (95.2%), in the field of heart disease (46.7%) and post-market trials (76.2%). Compared with pre-market RCTs, post-market RCTs showed more diverse adaptive designs such as response-adaptive randomization and adaptive enrichment. Conclusion: The average annual proportions of adaptive medical device RCTs in ClinicalTrials.gov has reduced in the last 10years. The most-used adaptive designs in medical device RCTs are GSD, SSR and investigating both superiority and non-inferiority.

Output feedback control for non-strict feedback nonlinear systems: an adaptive fuzzy backstepping scheme

The adaptive fuzzy tracking control design issue is considered in this paper for a class of non-strict feedback nonlinear systems subject to external disturbances. A fuzzy high-gain state observer is constructed to reconstruct the unmeasurable states of the system by leveraging the universal approximation property of fuzzy logic systems for arbitrary unknown nonlinear functions. Within the framework of backstepping control design and in conjunction with adaptive techniques, an adaptive fuzzy control approach is presented. Subsequently, to work out the inherent ‘complexity explosion’ problem of the proposed control approach, dynamic surface control technique is introduced, leading to a simplified adaptive fuzzy output feedback control scheme. Stability analysis shows that the two proposed control schemes can ensure that all signals of the closed-loop system are semi-globally uniformly ultimately bounded, meanwhile the system tracking error can converge to a small neighborhood around the origin. Ultimately, a numerical simulation example is provided to validate the feasibility of the proposed control scheme.

Personalized UI Layout Generation using Deep Learning: An Adaptive Interface Design Approach for Enhanced User Experience

This study presents a new approach to personalized UI design using deep learning techniques to improve user experience through interface customization. We propose a hybrid VAE-GAN architecture combining variational autoencoders and generative adversarial networks to create coherent and user-specific UI layouts. The system includes user-friendly electronic models that capture personal preferences and behaviors, enabling real-time personalization of interactions. Our methodology leverages large-scale UI design datasets, and user interaction logs to train and evaluate the model. Experimental results demonstrate significant improvements in layout quality, personalization accuracy, and user satisfaction compared to existing approaches. A customer research study with 200 participants from different cultures proves the effectiveness of the personalization model in real situations. The system achieves a personalization accuracy of 0.89 ± 0.03 and a transfer speed of 1.2s ± 0.1s, the most efficient state-of-the-art UI personalization system. In addition, we discuss the theoretical implications of our approach to UI/UX design principles, potential business applications, and ethical considerations around AI-driven identity. This research contributes to advancing adaptive interface design and opens up new ways to integrate deep learning with UI/UX processes.

BIOMIMETIC APPROACH FOR ADAPTIVE, RESPONSIVE AND KINETIC BUILDING FACADES: A BIBLIOMETRIC REVIEW OF EMERGING TRENDS

Nature serves as a data source for many disciplines, offering strategies such as sensitivity, adaptability, robustness, high resilience, lightness, energy efficiency, self-healing and self-improvement. The characteristics within this data pool provide alternatives for solving similar problems in different disciplines. Terms such as biomimicry, bio-informed, bio-inspired, nature-inspired, used to denote the process of deriving solutions from natural strategies, involve learning and adapting from nature. It is recognized as a significant tool not only in fields such as engineering, materials and robotics but also in the advancement of architectural knowledge. In recent years, research has prominently focused on adaptive, adaptive and kinetic biomimetic facade designs for energy efficiency. Within the scope of this study, publications on adaptive, adaptive and kinetic biomimetic facades were systematically reviewed and the state of the literature and contributions to the field were highlighted using bibliometric methods. In this context, a search was conducted in the Scopus database using predefined keywords to access relevant studies, evaluating aspects such as publication years, types of publications, countries of publication, Scopus category of the article, journal names and frequently used keywords. Subsequently, a content analysis was conducted for the most cited studies. As a result, it was observed that biomimetic adaptive/sensitive/kinetic facade designs in architecture, particularly in the context of energy efficiency, contribute to innovative studies, addressing aspects such as thermoregulation, natural ventilation and the use of renewable materials.

Sequential adaptive design for emulating costly computer codes

Gaussian processes (GPs) are generally regarded as the gold standard surrogate model for emulating computationally expensive computer-based simulators. However, the problem of training GPs as accurately as possible with a minimum number of model evaluations remains challenging. We address this problem by suggesting a novel adaptive sampling criterion called VIGF (variance of improvement for global fit). The improvement function at any point is a measure of the deviation of the GP emulator from the nearest observed model output. At each iteration of the proposed algorithm, a new run is performed where VIGF is the largest. Then, the new sample is added to the design and the emulator is updated accordingly. A batch version of VIGF is also proposed which can save the user time when parallel computing is available. Additionally, VIGF is extended to the multi-fidelity case where the expensive high-fidelity model is predicted with the assistance of a lower fidelity simulator. This is performed via hierarchical kriging. The applicability of our method is assessed on a bunch of test functions and its performance is compared with several sequential sampling strategies. The results suggest that our method has a superior performance in predicting the benchmark functions in most cases. An implementation of VIGF is available in the dgpsi R package, which can be found on CRAN.

An adaptive variable-parameter dynamic learning network for solving constrained time-varying QP problem

To efficiently solve the time-varying convex quadratic programming (TVCQP) problem under equational constraint, an adaptive variable-parameter dynamic learning network (AVDLN) is proposed and analyzed. Being different from existing varying-parameter and fixed-parameter convergent-differential neural network (VPCDNN and FPCDNN), the proposed AVDLN integrates the error signals into the time-varying parameter term. To do so, the TVCQP problem is transformed into a time-varying matrix equation. Second, an adaptive time-varying design formulation is designed for the error function, and then, the error function is integrated into the time-varying parameter. Furthermore, the AVDLN is designed with the adaptive time-varying design formulation. Moreover, the convergence and robustness theorems of AVDLN are proved by Lyapunov stability analysis, and Mathematical analysis demonstrates that AVDLN possesses a smaller upper bound on the convergence error and a faster error convergence rate than FPCDNN and VPCDNN. Finally, the validity of AVDLN is demonstrated by simulations, and the comparative results prove that the proposed AVDLN has a faster convergence speed and smaller error fluctuation.

Advances in Deep Reinforcement Learning for Computer Vision Applications

Deep Reinforcement Learning (DRL) has become very popular for computer vision (CV), solving mostly visually complex environments with decision making and dynamic adaption to different situations. This article provides an introduction to the basic concepts of deep reinforcement learning with a special focus on their applications in computer vision tasks, including challenging problems and emerging solutions. It reviews various DRL algorithms such as Q-learning, policy gradient methods, and Actor-Critic models, explaining much of the modifications that are done to make it work on high-dimensional visual problems. Different applications of DRL in major CV applications like object detection, image segmentation, target tracking and image generation are reviewed to demonstrate the power as well as limitations of DRL in practice. More importantly, the novel paradigms such as hierarchical policy learning, adaptive reward design multi-task reinforcement learning and domain adaptation can be viewed as promising premises to improve model efficiency and generalizability in multiple scenarios. Finally, this paper mentions a few of the existing challenges like computational power cost and sample efficiency; as well as future paths for enhancing that can widen devout reinforcement learning in computer vision. Through this comprehensive overview, we aim to shed light on the promising synergies between DRL and CV, while identifying key areas for future research and application.

Adaptive Observer Design for Heat PDEs With Sensor Delay and Parameter Uncertainties

Adaptive Observer Design for Heat PDEs With Sensor Delay and Parameter Uncertainties

A Bayesian adaptive feasibility design for rare diseases

It is important for researchers to carefully assess the feasibility of a clinical trial prior to the launch of the study. One feasibility aspect that needs to be considered includes whether investigators can expect to successfully achieve the sample size needed for their trial. In this manuscript, we present a Bayesian design in which data collected during a pilot study is used to predict the feasibility of a planned phase III trial. Specifically, we outline a model that predicts a target sample size obtained from the Gamma-Poisson distribution. In a simulation study, we showcase the utility of the proposed design by applying it to a phase III trial designed to assess the efficacy of mycophenolate mofetil in individuals with mild systemic sclerosis. We demonstrate that the predictive nature of the proposed design is particularly useful for rare disease clinical trials and has the potential to greatly increase their efficiency.

Differential neural network based adaptive average output feedback control design for dosage determination on cancer based immunotherapy treatment

Immunotherapy involves natural and synthetic substances to stimulate the body’s immune response. This treatment approach is practical not only for addressing immune deficiencies but also for combating malignancies. This paper describes a non-parametric approximated adaptive control process for managing cancer dynamics under immunotherapy treatment, utilizing a combination of a differential neural network (DNN) observer and nonlinear control techniques such as sliding mode and local optimal strategies. By employing the state estimation and control methods, close tracking between the estimated states provided by the neural network and the cancer model dynamics is possible. Internal model reconstruction and an observer provided by a variable structure model are essential for controlling unknown plants. Furthermore, the control design has successfully reduced tumor cells despite uncertainties and external perturbations affecting cancer dynamics. This robustness enhances the reliability of the proposed design. A virtual real-time scheme was developed to demonstrate this controller’s feasibility in real clinical scenarios. In this scheme, a simulated patient generates variables of immunotherapy dynamics as electrical signals, which are then analyzed by a real-time project.

Adaptable downstream processing design for recovery of butanediols after fermentation

Adaptable downstream processing design for recovery of butanediols after fermentation

Macrophage Therapy for Acute Liver Injury (MAIL): a study protocol for a phase 1 randomised, open-label, dose-escalation study to evaluate safety, tolerability and activity of allogeneic alternatively activated macrophages in patients with paracetamol-induced acute liver injury in the UK

IntroductionAcute liver failure (ALF) has no effective treatment other than liver transplantation and is commonly caused by paracetamol overdose. New treatments are needed to treat and prevent ALF. Alternatively-activated macrophages...

Advancements in Artificial Intelligence-mediated Fabrication of 3D, 4D, and 5D Printed for Fabrication of Drug Delivery Formulations

: One of the most powerful and inventive fabrication techniques used to create novel structures and solid materials using precise additive manufacturing technology is 5D and 4D printing, which is an improved version of 3D printing. It catches people's attention because of its capacity to generate fast, highly complex, adaptable product design and fabrication. Real-time sensing, change adaptation, and printing state prediction are made possible by this technology with the use of artificial intelligence (AI). The process of 3D printing involves the use of sophisticated materials and computer-aided design (CAD) with tomography scanning controlled by artificial intelligence (AI). The printing material is deposited according to the specifications of the file, typically in STL format; however, the printing process takes time.4D printing, which incorporates intelligent materials with time as a fourth dimension, can solve this drawback. About 80% of the time will be saved by this technique's self-repair and self-assembly qualities. One limitation of 3D printing is that it cannot print complex shapes with curved surfaces. However, this limitation can be solved by using 5D printing, which uses rotation of the print bed and extruder head to achieve additive manufacturing in five different axes. Some printed materials are made sensitive to temperature, humidity, light, and other parameters so they can respond to stimuli. With its effective and efficient manufacturing for the necessary design precision, this review assesses the potential of these procedures with AI intervention in medicine and pharmacy.

CMIP6 multi-model ensemble projection of reference evapotranspiration using machine learning algorithms

Changes in reference crop evapotranspiration (ETo) due to climate change (CC) can severely impact food and water security, emphasizing the need for integrating ETo projections into agricultural water management strategies. In this study, ETo changes were projected for two future time slices with respect to the baseline using several machine learning techniques, incorporating minimum and maximum temperature, diurnal temperature range, and extraterrestrial radiation across Iran. Additionally, an ensemble of 10 CMIP6 Global Climate Models, downscaled by the NASA Earth Exchange Global Daily Downscaled Projections (NEX-GDDP-CMIP6), was employed. The X-means clustering algorithm was also exploited to classify ETo based on various characteristics, including minimum, maximum, average, skewness, and standard deviation, as well as ETo ranges of 0–5, 5–10, and greater than 10 mm d⁻¹. This clustering approach divided the study area into five distinct clusters. Apart from cluster I, where the Support Vector Machine outperformed, the Random Forest technique provided more accurate ETo predictions. The findings project an average ETo increase of 4.8 % and 5.3 % during 2030–2049, and 8.0 % and 13.3 % for 2080–2099 under SSP245 and SSP585, respectively. Geographically, the highest ETo increases are anticipated primarily in the northern and western parts of the country, predominantly within clusters I and II. Notably, the ETo rise will exceed 40 % relative to the baseline during the late century under the SSP585. Furthermore, the most significant ETo increment is expected during winter. Future projections also indicate that cluster V, which already experiences significant daily ETo peaks, will face even more ETo extremes. Given the critical importance of these regions for sustaining food and water security and preserving natural resources, the substantial rise in ETo under future CC poses a significant threat to natural sustainability in Iran. This highlights the critical necessity for adaptive strategies in agricultural water management to mitigate the adverse CC effects. In this context, the current findings can assist decision-makers in identifying hotspots and quantifying CC impacts, thereby enabling the design of crucial adaptations.

Single and multi-objective real-time optimisation of an industrial injection moulding process via a Bayesian adaptive design of experiment approach

Minimising cycle time without inducing quality defects is a major challenge in injection moulding (IM). Design of Experiment methods (DoE) have been widely studied for optimisation of injection moulding, however existing methods have limitations, including the need for a large number of experiments within a pre-determined search space. Bayesian adaptive design of experiment (ADoE) is an iterative process where the results of the previous experiments are used to make an informed selection for the next design. In this study, an experimental ADoE approach based on Bayesian optimisation was developed for injection moulding using process and sensor data to optimise the quality and cycle time in real-time. A novel approach for the real-time characterisation of post-production shrinkage was introduced, utilising in-mould sensor data on temperature differential during part cooling. This characterisation approach was verified by post-production metrology results. A single and multi-objective optimisation of the cycle time and temperature differential (\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\Delta T$$\\end{document}) in an injection moulded component is proposed. The multi-objective optimisation techniques, composite desirability function and Nondominated Sorting Genetic Algorithm (NSGA-II) using the Response Surface Methodology (RSM) model, are compared with the real-time novel ADoE approach. ADoE achieved almost a 50\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\%$$\\end{document} reduction in the number of experiments required for the single optimisation of \\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\Delta T$$\\end{document}, and an almost 30\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\%$$\\end{document} decrease for the optimisation of \\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\Delta T$$\\end{document} and cycle time together compared to composite desirability function and NSGA-II. The optimal settings identified by ADoE for multiobjective optimisation were similar to the selected Pareto optimal solution found by NSGA-II.

Preparation and Adsorption Properties of a Three-Dimensional Superhydrophilic Mercury-Ion-Imprinted Polymer with Dual Recognition Site Based on MoS2/SBA-15.

Water pollution resulting from Hg(II) ions has garnered significant global concern for public health. The flexibility and simplicity of design, cost savings, and ease of operation with adaptive designs provide adsorption with a considerable advantage over other processes. However, MoS2 is hydrophobic in nature, which limits its efficiency in the removal of Hg(II) ions from water. Therefore, the incorporation of hydrophilic SBA-15 as supporting material, combined with a nanoflower-like layered MoS2 and its highly reactive exposed edges, produces hydrophilic properties that effectively eliminate Hg(II) from water . A mercury-ion-imprinted polymer (Hg(II)-IIP) was synthesized on the MoS2/SBA-15 surface using N-allylthiourea (ATU) as a functional monomer to enhance material selectivity and create dual recognition sites. Characterization investigation using scanning electron microscopy (SEM), X-ray diffraction (XRD), Fourier transform infrared (FTIR), X-ray photoelectron spectroscopy (XPS), and water contact angle showed the successful synthesis of Hg(II)-IIP, excellent hydrophilicity, and close interaction between MoS2 and SBA-15. The maximum adsorption capacity of Hg(II)-IIP for Hg(II) under optimized adsorption conditions was 567.78 mg·g-1 in 90 min, which was 6 times greater than that of the IIP solely based only on SBA-15, and followed by pseudo-second-order and aligned more closely fits with the Langmuir adsorption isotherm. Furthermore, the adsorption capacity of the synthesized Hg(II)-IIP was greater than that of three other common monomers IIP. Hg(II)-IIP possesses a strong ability to regenerate, while also showing better selectivity for Hg(II) in the presence of other interfering ions, indicating its robust anti-interference capability in the multivariate mixed solution. The analysis of the actual sample indicated that Hg(II)-IIP attained recoveries between 97.92 and 100.28% in water samples. Theoretical calculations using density functional theory (DFT) and frontier molecular orbital (FMO) indicated that the binding energy of the sulfur atom forming a tetra-ligand with Hg(II) on ATU was 0.6511 eV greater than that of a diligand. The energy gap was determined to be 0.1550 eV, supporting the preference for S-Hg tetra-ligand adsorption.

Phosphatidylethanol measures in patients with severe COVID-19-associated respiratory failure identify a subset with alcohol misuse.

Clinical trials in patients with COVID-19 have exclusively used self- or proxy-reporting to characterize alcohol consumption. The aim of this study was to measure an objective biomarker of recent alcohol use in patients hospitalized with severe COVID-19-associated respiratory failure who were enrolled in an investigational clinical trial to determine the prevalence of alcohol misuse, and to explore the relationship of alcohol use with outcomes. We conducted a substudy of patients enrolled in the multicenter, phase 2, adaptive platform design, Investigation of Serial Studies to Predict Your Therapeutic Response with Imaging And molecular Analysis in COVID-19 trial (ClinicalTrials.gov: NCT04488081), conducted at 20 hospital systems across the United States. Three hundred and fifty-five patients with available red blood cell (RBC) samples and 60-day follow-up assessments were included. RBCs were utilized to measure phosphatidylethanol (PEth). Prespecified thresholds of PEth were utilized to stratify patients into groups: low/no alcohol use (PEth < 20 ng/mL), significant alcohol use (PEth 20-200 ng/mL), and heavy alcohol use (PEth ≥ 200 ng/mL). In this cohort, 17% of patients met criteria for significant alcohol use, while 4% met criteria for heavy alcohol use. Alcohol misuse was associated with diminished odds for home discharge, though this finding did not achieve statistical significance. In a cohort of patients with severe COVID-19 enrolled in a clinical trial, alcohol consumption of two or more standard drinks per day was present among 21%, approximating the proportion of patients with diabetes, and raising the possibility that alcohol consumption alters risk for severe viral pneumonia. Undetected alcohol misuse among clinical trial participants has the potential to influence study outcomes or contribute to adverse events.

Prospects and Challenges of Affective AI Technology in Supporting Remote Learning

This study analyzes the potential and challenges of introducing affective AI technologies to promote remote education. The growing popularity of remote learning highlights the significant upside of affective AI in real-time emotional monitoring and feedback, especially for improving learning motivation, optimizing personalized educational paths, and supporting students' mental health. This study explores the particular applications of affective AI technologies and the ethical and privacy concerns they present. The research indicates that affective AI technology, based on the principles of affective computing, may strengthen students' emotional management abilities, improve the overall educational experience by identifying their emotional states in real time, and create more possibilities for teacher-student interactions. Affective AI enhances student motivation and optimizes personalized learning paths in remote education, especially for fostering positive emotional regulation and mental health support outcomes. However, affective AI encounters challenge related to technical prejudice, privacy protection, ethical fairness, and user acceptance in extensive applications, particularly notable disparities in multicultural contexts, which require solutions through privacy safeguards, algorithm refinement, and culturally adaptive design. This study is significant as it systematically reveals the potential of affective AI technologies in remote learning, identifies their critical challenges, and offers valuable recommendations and references for future advancement in global education.

Covariate-adjusted response-adaptive designs for semiparametric survivalmodels.

Covariate-adjusted responseadaptive (CARA) designs are effective in increasing the expected number of patients receiving superior treatment inan ongoing clinical trial, given a patient's covariate profile. There has recently been extensive research on CARA designs with parametric distributional assumptions on patient responses. However, the range of applications for such designs becomes limited in real clinical trials. Sverdlov etal. have pointed out that irrespective of a specific parametric form of the survival outcomes, their proposed CARA designs based on the exponential model provide valid statistical inference, provided the final analysis is performed using the appropriate accelerated failure time (AFT) model. In real survival trials, however, the planned primary analysis is rarely conducted using an AFT model. The proposed CARA designs are developed obviating any distributional assumptions about the survival responses, relying only on the proportional hazards assumption between the two treatment arms. To meet the multiple experimental objectives of a clinical trial, the proposed designs are developed based on an optimal allocation approach. The covariate-adjusted doubly adaptive biased coin design and the covariate-adjusted efficient-randomized adaptive design are used to randomize the patients to achieve the derived targets on expectation. These expected targets are functions of the Cox regression coefficients that are estimated sequentially with the arrival of every new patient into the trial. The merits of the proposed designs are assessed using extensive simulation studies of their operating characteristics and then have been implemented to re-design a real-life confirmatory clinical trial.

Eye-tracking analysis to assess the mental load of unmanned aerial system operators: systematic review and future directions

Abstract This article presents a systematic review on the use of eye-tracking technology to assess the mental workload of unmanned aircraft system (UAS) operators. With the increasing use of unmanned aircraft in military and civilian operations, understanding the mental workload of these operators has become essential for ensuring mission effectiveness and safety. The review covered 26 studies that explored the application of eye-tracking to capture nuances of visual attention and assess cognitive load in real-time. Traditional methods such as self-assessment questionnaires, although useful, showed limitations in terms of accuracy and objectivity, highlighting the need for advanced approaches like eye-tracking. By analysing gaze patterns in simulated environments that reproduce real challenges, it was possible to identify moments of higher mental workload, areas of concentration and sources of distraction. The review also discussed strategies for managing mental workload, including adaptive design of human-machine interfaces. The analysis of the studies revealed a growing relevance and acceptance of eye-tracking as a diagnostic and analytical tool, offering guidelines for the development of interfaces and training that dynamically respond to the cognitive needs of operators. It was concluded that eye-tracking technology can significantly contribute to the optimisation of UAS operations, enhancing both the safety and efficiency of military and civilian missions.