United States Army Futures Command

Published with license by Taylor & Francis Group, LLC. This article aims to investigate the Grand Challenges which arise in the current and emerging landscape of rapid technological evolution towards more intelligent interactive technologies, coupled with increased and widened societal needs, as well as individual and collective expectations that HCI, as a discipline, is called upon to address. A perspective oriented to humane and social values is adopted, formulating the challenges in terms of the impact of emerging intelligent interactive technologies on human life both at the individual and societal levels. Seven Grand Challenges are identified and presented in this article: Human-Technology Symbiosis; Human-Environment Interactions; Ethics, Privacy and Security; Well-being, Health and Eudaimonia; Accessibility and Universal Access; Learning and Creativity; and Social Organization and Democracy. Although not exhaustive, they summarize the views and research priorities of an international interdisciplinary group of experts, reflecting different scientific perspectives, methodological approaches and application domains. Each identified Grand Challenge is analyzed in terms of: concept and problem definition; main research issues involved and state of the art; and associated emerging requirements. BACKGROUND This article presents the results of the collective effort of a group of 32 experts involved in the community of the Human Computer Interaction International (HCII) Conference series. The group’s collaboration started in early 2018 with the collection of opinions from all group members, each asked to independently list and describe five HCI grand challenges. During a one-day meeting held on the 20th July 2018 in the context of the HCI International 2018 Conference in Las Vegas, USA, the identified topics were debated and challenges were formulated in terms of the impact of emerging intelligent interactive technologies on human life both at the individual and societal levels. Further analysis and consolidation led to a set of seven Grand Challenges presented herein. This activity was organized and supported by the HCII Conference series.

<div class="htmlview paragraph">An experimental investigation of the effects of ceramic coatings on diesel engine performance and exhaust emissions was conducted. Tests were carried out over a range of engine speeds at full load for a standard metal piston and two pistons insulated with 0.5 mm and 1.0 mm thick ceramic coatings. The thinner (0.5 mm) ceramic coating resulted in improved performance over the baseline engine, with the gains being especially pronounced with decreasing engine speed. At 1000 rpm, the 0.5 mm ceramic coated piston produced 10% higher thermal efficiency than the metal piston. In contrast, the relatively thicker coating (1 mm), resulted in as much as 6% lower thermal efficiency compared to baseline. On the other hand, the insulated engines consistently presented an attractive picture in terms of their emissions characteristics. Due to the more complete combustion in the insulated configurations, exhaust CO levels were between 30% and 60% lower than baseline levels. Similarly, unburned HC levels were 35% to 40% lower for the insulated pistons. The NO<sub>x</sub> concentrations were also 10% to 30% lower due to the changed nature of combustion in the insulated engines. Finally, smoke emissions decreased slightly in the insulated engines.</div>

We argue that artificial intelligence (AI) systems should be designed with features that build trust by bringing decision-analytic perspectives into AI. Actionable and interpretable AI will incorporate explicit quantifications and visualizations of user confidence in AI recommendations.

New materials and chemical knowledge for improved personal protection are among the most pressing needs in the international community. Reported attacks using chemical warfare agents (CWAs,) including organophosphate soman (GD) and thioether mustard gas (HD) are driving research in field-deployable catalytic composites for rapid toxin degradation. In this work, we report simple template-free low temperature synthesis that enables for the first time, a deployable-structured catalytic metal-organic framework/polymer textile composite "MOF-fabric" showing rapid hydrolysis and oxidation of multiple active chemical warfare agents, GD and HD, respectively, and their simulants. Our method yields new zirconium-porphyrin based nano-crystalline PCN-222 MOF-fabrics with adjustable MOF loading and robust mechanical adhesion on low-cost nonwoven polypropylene fibers. Importantly, we describe quantitative kinetic analysis confirming that our MOF-fabrics are as effective as or better than analogous MOF powders for agent degradation, especially for oxidation. Faster oxidation using the MOF-fabrics is ascribed to the composite geometry, where active MOF catalysts are uniformly displayed on the MOF-textile enabling better reactant transport and reactive oxidant generation. Furthermore, we note the discovery of visible photo-activation of GD hydrolysis by a MOF-fabric, which is ascribed to oxidation at the active metal node site, significantly increasing the rate over that observed without illumination. These results provide important new insights into the design of future materials and chemical systems to protect military, first-responders, and civilians upon exposure to complex chemical toxins.

Noise, vibration, and harshness (NVH) issue in switched reluctance machines (SRMs), originating from their doubly salient structure and unique principle of operation, is addressed in this work by proposing a structural design modification in the stator, which increases stiffness to mass ratio of the structure. A 24-slot 16-pole (24s/16p) SRM designed with the aim of automotive application is studied here for the NVH optimization, at different target operating points, using stator pole bridges. Stator pole bridges link consecutive stator teeth to provide additional stiffness to the stator structure. Average torque reduction due to flux shorting in stator pole bridges is tackled by proposing a low-permeability material, with considerable stiffness, which has not yet seen its' application in SRM NVH domain. Multiphysics aspects of stator pole bridge design encompassing electromagnetic radial force, mechanical stress, steady-state temperature distribution, and acoustic noise analyses are presented in this article. Possible manufacturing issues are considered during the design phase and appropriate measures are implemented to facilitate easier construction of two 100-kW prototypes. The final design with stator pole bridges and a baseline design without any stator pole bridges are prototyped, after rigorous multiphysics optimization, for extensive testing. Experimental results verify simulation outputs and report a maximum noise reduction of 12.52 dBA in the stator pole bridge model compared to the baseline SRM.

Abstract This study utilizes Raman spectroscopy to analyze the burn-induced collagen conformational changes in ex vivo porcine skin tissue. Raman spectra of wavenumbers 500–2000 cm −1 were measured for unburnt skin as well as four different burn conditions: (i) 200 °F for 10 s, (ii) 200 °F for the 30 s, (iii) 450 °F for 10 s and (iv) 450 °F for 30 s. The overall spectra reveal that protein and amino acids-related bands have manifested structural changes including the destruction of protein-related functional groups, and transformation from α-helical to disordered structures which are correlated with increasing burn severity. The deconvolution of the amide I region (1580–1720 cm −1 ) and the analysis of the sub-bands reveal a change of the secondary structure of the collagen from the α-like helix dominated to the β-aggregate dominated one. Such conformational changes may explain the softening of mechanical response in burnt tissues reported in the literature.

This article presents a real-time approach for classification of burn depth based on B-mode ultrasound imaging. A grey-level co-occurrence matrix (GLCM) computed from the ultrasound images of the tissue is employed to construct the textural feature set and the classification is performed using nonlinear support vector machine and kernel Fisher discriminant analysis. A leave-one-out cross-validation is used for the independent assessment of the classifiers. The model is tested for pair-wise binary classification of four burn conditions in ex vivo porcine skin tissue: (i) 200 °F for 10 s, (ii) 200 °F for 30 s, (iii) 450 °F for 10 s, and (iv) 450 °F for 30 s. The average classification accuracy for pairwise separation is 99% with just over 30 samples in each burn group and the average multiclass classification accuracy is 93%. The results highlight that the ultrasound imaging-based burn classification approach in conjunction with the GLCM texture features provide an accurate assessment of altered tissue characteristics with relatively moderate sample sizes, which is often the case with experimental and clinical datasets. The proposed method is shown to have the potential to assist with the real-time clinical assessment of burn degrees, particularly for discriminating between superficial and deep second degree burns, which is challenging in clinical practice.

Abstract Virtual reality (VR) simulator has emerged as a laparoscopic surgical skill training tool that needs validation using brain–behavior analysis. Therefore, brain network and skilled behavior relationship were evaluated using functional near-infrared spectroscopy (fNIRS) from seven experienced right-handed surgeons and six right-handed medical students during the performance of Fundamentals of Laparoscopic Surgery (FLS) pattern of cutting tasks in a physical and a VR simulator. Multiple regression and path analysis (MRPA) found that the FLS performance score was statistically significantly related to the interregional directed functional connectivity from the right prefrontal cortex to the supplementary motor area with F (2, 114) = 9, p < 0.001, and R 2 = 0.136. Additionally, a two-way multivariate analysis of variance (MANOVA) found a statistically significant effect of the simulator technology on the interregional directed functional connectivity from the right prefrontal cortex to the left primary motor cortex ( F (1, 15) = 6.002, p = 0.027; partial η 2 = 0.286) that can be related to differential right-lateralized executive control of attention. Then, MRPA found that the coefficient of variation (CoV) of the FLS performance score was statistically significantly associated with the CoV of the interregionally directed functional connectivity from the right primary motor cortex to the left primary motor cortex and the left primary motor cortex to the left prefrontal cortex with F (2, 22) = 3.912, p = 0.035, and R 2 = 0.262. This highlighted the importance of the efference copy information from the motor cortices to the prefrontal cortex for postulated left-lateralized perceptual decision-making to reduce behavioral variability.

Garments protective against chemical warfare agents (CWAs) or accidently released toxic chemicals must block the transport of toxic gases/vapors for a substantial time and allow moisture transport for breathability. These demands are challenging: either the barriers block CWAs effectively but have poor breathability or barriers have excellent breathability but cannot block CWAs well. Existing protective garments employ large amounts of active carbon, making them quite heavy. Metal-organic framework (MOF)-based adsorbents are being investigated as sorbents for CWAs. Breathable laminate of graphene oxide (GO) flakes supported on a porous membrane reduces permeation rates of CWA simulants substantially. We developed a multilayered membrane-based flexible barrier: GO laminate-based membrane over a MOF nanocrystal-filled expanded polytetrafluorethylene (ePTFE) membrane having submicrometer pores. The GO laminate-based layer developed a steady breakthrough concentration level almost 2 orders of magnitude below the usual breakthrough level. This highly reduced level of CWA was blocked by the MOF nanocrystal-filled membrane substrate layer over a highly extended period. We demonstrated the blocking of CWAs, mustard (HD), soman (GD), a sarin simulant [dimethyl methyl phosphonate (DMMP)], and ammonia for an extended period while the moisture transmission rate was substantial. The times for complete blockage of ammonia, HD, GD, and DMMP were 2750 min, 1075 min, 176 min, and 7 days, respectively. This remarkable performance resulted from a very low steady-state penetrant permeation through GO-laminate membrane and substantial penetrant sorption by MOF nanocrystals; furthermore, both layers show high moisture vapor transmission.

Error-based learning is one of the basic skill acquisition mechanisms that can be modeled as a perception-action system and investigated based on brain-behavior analysis during skill training. Here, the error-related chain of mental processes is postulated to depend on the skill level leading to a difference in the contextual switching of the brain states on error commission. Therefore, the objective of this paper was to compare error-related brain states, measured with multi-modal portable brain imaging, between experts and novices during the Fundamentals of Laparoscopic Surgery (FLS) "suturing and intracorporeal knot-tying" task (FLS complex task)-the most difficult among the five psychomotor FLS tasks. The multi-modal portable brain imaging combined functional near-infrared spectroscopy (fNIRS) and electroencephalography (EEG) for brain-behavior analysis in thirteen right-handed novice medical students and nine expert surgeons. The brain state changes were defined by quasi-stable EEG scalp topography (called microstates) changes using 32-channel EEG data acquired at 250 Hz. Six microstate prototypes were identified from the combined EEG data from experts and novices during the FLS complex task that explained 77.14% of the global variance. Analysis of variance (ANOVA) found that the proportion of the total time spent in different microstates during the 10-s error epoch was significantly affected by the skill level (p < 0.01), the microstate type (p < 0.01), and the interaction between the skill level and the microstate type (p < 0.01). Brain activation based on the slower oxyhemoglobin (HbO) changes corresponding to the EEG band power (1-40 Hz) changes were found using the regularized temporally embedded Canonical Correlation Analysis of the simultaneously acquired fNIRS-EEG signals. The HbO signal from the overlying the left inferior frontal gyrus-opercular part, left superior frontal gyrus-medial orbital, left postcentral gyrus, left superior temporal gyrus, right superior frontal gyrus-medial orbital cortical areas showed significant (p < 0.05) difference between experts and novices in the 10-s error epoch. We conclude that the difference in the error-related chain of mental processes was the activation of cognitive top-down attention-related brain areas, including left dorsolateral prefrontal/frontal eye field and left frontopolar brain regions, along with a 'focusing' effect of global suppression of hemodynamic activation in the experts, while the novices had a widespread stimulus(error)-driven hemodynamic activation without the 'focusing' effect.

Commercial off-the-shelf (COTS)-centric cyber physical systems often contain software and hardware elements with life-spans shorter than the systems’ intended life-span. Various studies have examined hardware obsolescence, although in most systems, software costs contribute as much, or more, to the total life cycle costs than hardware. The aim of our research effort is to explore, synthesize, and compile past research efforts on obsolescence in the context of COTS-based systems, and propose new ways to overcome related issues. This research effort suggests the need for systematic perspectives to streamline potentially overbearing acquisition processes while focusing on core critical aspects affecting systems sustainment and cost. Significant life cycle costs associated with obsolescence mitigation approaches, therefore, programmatic strategic planning should be adapted to include the context of obsolescence with the objective to improve the efficiency of new COTS-intensive CPS systems with enduring perspectives. The study reveals opportunities and challenges for obsolescence in COTS-based CPSs.

The recent use of organophosphate nerve agents in Syria, Malaysia, Russia, and the United Kingdom has reinforced the potential threat of their intentional release. These agents act through their ability to inhibit human acetylcholinesterase (hAChE; E.C. 3.1.1.7), an enzyme vital for survival. The toxicity of hAChE inhibition via G-series nerve agents has been demonstrated to vary widely depending on the G-agent used. To gain insight into this issue, the structures of hAChE inhibited by tabun, sarin, cyclosarin, soman, and GP were obtained along with the inhibition kinetics for these agents. Through this information, the role of hAChE active site plasticity in agent selectivity is revealed. With reports indicating that the efficacy of reactivators can vary based on the nerve agent inhibiting hAChE, human recombinatorially expressed hAChE was utilized to define these variations for HI-6 among various G-agents. To identify the structural underpinnings of this phenomenon, the structures of tabun, sarin, and soman-inhibited hAChE in complex with HI-6 were determined. This revealed how the presence of G-agent adducts impacts reactivator access and placement within the active site. These insights will contribute toward a path of next-generation reactivators and an improved understanding of the innate issues with the current reactivators.

The internet of battlefield things (IoBT) is expected to be a major feature of future tactical wireless networks. Multiple challenges arise from the expected scale, heterogeneity, information sharing (in a joint or coalition environment), dynamics, and actions of sophisticated adversaries. The chapter will discuss these challenges in detail, and explore emerging directions which provide opportunities to enable a scalable, secure, and performant IoBT.

Significant progress in the development of nonreciprocal optical components with broken Kirchhoff symmetry paves the way for increasing the photovoltaic (PV) conversion efficiency beyond the Shockley-Queisser limit due to reuse of emitted photons. Recent papers have analyzed the PV converter with several or an infinite number of multijunction cells, in which the cells are coupled via nonreciprocal filters (optical diodes) in such a way that the light emitted by one cell is absorbed by another cell. We proposed and investigated a single cell converter with nonreciprocal external photon recycling, which provided reabsorption and reuse of the emitting light by the same cell. We considered properties of photons in the sunbeam in terms of ergodicity, disorder, energy availability, information entropy, and coherence, and established fundamental limitations imposed by endoreversible thermodynamics on conversion efficiency at maximal power output. Our results show that the nonreciprocal converter with an ideal multijunction cell can approach the Carnot efficiency, whereas operating exactly at the Carnot limit requires an infinite number of photon recycling processes. This requirement resolves the famous thermodynamic paradox of the optical diode because any small dissipation in the cell or optical system enhanced by infinite recycling will stabilize the converter operation below the Carnot limit. We generalized endoreversible thermodynamics to photonic distributions with nonzero chemical potential and derived the limiting efficiency of the nonreciprocal single-junction PV converter. The performance of this converter with available GaAs solar cells was evaluated.

INTRODUCTION: Within the Military Health System, the process of transporting patients from an initial point of injury and throughout the entire continuum of care is called "en route care." A Committee on En Route Combat Casualty Care was established in 2016 as part of the DoD Joint Trauma System to create practice guidelines, recommend training standards, and identify research priorities within the military en route care system. MATERIALS AND METHODS: Following an analysis of currently funded research, future capabilities, and findings from a comprehensive scoping study, members of a sub-working group for research identified the top research priorities that were needed to better guide evidence-based decisions for practice and policy, as well as the future state of en route care. RESULTS: Based on the input from the entire committee, 10 en route care research topics were rank-ordered in the following manner: (1) medical documentation, (2) clinical decision support, (3) patient monitoring, (4) transport physiology, (5) transfer of care, (6) maintaining normothermia, (7) transport timing following damage control resuscitation or surgery, (8) intelligent tasking, (9) commander's risk assessment, and (10) unmanned transport. Specific research questions and technological development needs were further developed by committee members in an effort to guide future research and development initiatives that can directly support operational en route care needs. The research priorities reflect three common themes, which include efforts to enhance or increase care provider capability and capacity; understand the impact of transportation on patient physiology; and increase the ability to coordinate, communicate, and facilitate patient movement. Technology needs for en route care must support interoperability of medical information, equipment, and supplies across the global military health system in addition to adjusting to a dynamic transport environment with the smallest possible weight, space, and power requirements. CONCLUSIONS: To ensure an evidence-based approach to future military conflicts and other medical challenges, focused research and technological development to address these 10 en route care research gaps are urgently needed.

IoBTs must feature collaborative, context-aware, multi-modal fusion for real-time, robust decision-making in adversarial environments. The integration of machine learning (ML) models into IoBTs has been successful at solving these problems at a small scale (e.g., AiTR), but state-of-the-art ML models grow exponentially with increasing temporal and spatial scale of modeled phenomena, and can thus become brittle, untrustworthy, and vulnerable when interpreting large-scale tactical edge data. To address this challenge, we need to develop principles and methodologies for uncertainty-quantified neuro-symbolic ML, where learning and inference exploit symbolic knowledge and reasoning, in addition to, multi-modal and multi-vantage sensor data. The approach features integrated neuro-symbolic inference, where symbolic context is used by deep learning, and deep learning models provide atomic concepts for symbolic reasoning. The incorporation of high-level symbolic reasoning improves data efficiency during training and makes inference more robust, interpretable, and resource-efficient. In this paper, we identify the key challenges in developing context-aware collaborative neuro-symbolic inference in IoBTs and review some recent progress in addressing these gaps.

Modern day tactical battlefield networks are typically not designed specific to the operational mission they support. These tactical networks are usually large, flat, non-segmented, absent of a robust cybersecurity response process, and overloaded with heterogeneous systems with various bandwidth and Quality of Service (QoS) requirements. These conditions create poor battlefield network performance and introduce significant risk to commanders and their Warfighters. To address these deficiencies we have designed a framework that uses network slicing to address many of the cybersecurity and performance issues unique to modern day tactical battlefield networks. Novel communication technology in 5G networking has the potential to be adapted to work with existing and next-generation tactical radio technologies to create network slices that provide high security, strong segmentation, and guaranteed quality of service performance in both intra and inter-slice communication. Our novel framework contains three components: 1) An intelligent network slice controller that dynamically manages the creation, management, security, and topology of tactical battlefield network slices; 2) a decision making system that continuously analyzes the battlefield network and suggest dynamic reconfiguration based upon network changes, threats, losses, or mission modifications, adjustments, or pivots; and 3) a network slice broker and orchestrator that implements the slice reconfiguration in real-time when it is necessary. In addition to the framework described in this paper, the applicability and contributions to Joint All Domain Command and Control (JADC2) are presented as well as potential challenges in our future work.

This paper introduces a novel optimum current profile generation technique based on the simultaneous reduction of torque ripple and radial force ripple for acoustic noise and vibration reduction in Switched Reluctance Machines (SRMs). Optimum current profiles at various operating points are generated through electromagnetic finite element analysis (FEA) aided dynamic simulations. Multi-physics simulations are performed to evaluate the NVH performance of the SRM drive for both conventional and proposed current waveforms, which show significant comparative improvements for the proposed methodology. A prototype SRM rated at 100 <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">kW</i> power with a maximum operating speed of 4250 <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">rpm</i> is constructed for testing. The proposed method is tested against the conventional current profiling technique at numerous operating points, encompassing a wide operation speed range. Test results are cross matched against simulation results, which validates the accuracy of the implemented modeling process. Moreover, test results show significant improvement in NVH performance and validate the effectiveness of the proposed methodology. A peak improvement of 19.93 <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">dBA</i> is seen from experimental results for the proposed current profiling technique at 800 <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">rpm</i> 200 <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Nm</i> output torque condition.

The Internet of Battlefield Things (IoBT) is a dynamically composed network of intelligent sensors and actuators that operate as a command and control, communications, computers, and intelligence complex-system with the aim to enable multi-domain operations. The use of artificial intelligence can help transform the IoBT data into actionable insight to create information and decision advantage on the battlefield. In this work, we focus on how accounting for uncertainty in IoBT systems can result in more robust and safer systems. Human trust in these systems requires the ability to understand and interpret how machines make decisions. Most real-world applications currently use deterministic machine learning techniques that cannot incorporate uncertainty. In this work, we focus on the machine learning task of classifying vehicles from their audio recordings, comparing deterministic convolutional neural networks (CNNs) with Bayesian CNNs to show that correctly estimating the uncertainty can help lead to robust decision-making in IoBT.

ABSTRACT This paper presents the advancements in manufacturing science and the engineering learned because of the global emergencies resulting from pandemics. Established manufacturing processes strained to the limit delivering parts and services during the pandemic in industrialized as well as industrializing nations. These limitations call for manufacturing by integrating or hybridizing multiple processes and sometimes materials. This paper illustrates value propositions resulting from hybrid manufacturing by using pertinent case studies of a ventilator filter housing and an injection molding tool. This paper concludes by making a case for convergence of heterogenous materials, processes, and systems in a unified platform allowing adaptability, agility, and flexibility in manufacturing geared toward offering resilience in similar future global catastrophes.