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Artificial intelligence (AI) technologies such as image recognition, classification, and generative AI are constantly evolving rapidly. Many of these techniques operate in high-performance computing environments because they use complex architectural models and millions of parameters to improve inference and prediction performance. In recent years, there has been a growing demand for AI applications in the defense domain, and considerable research has been conducted. In tactical environments, images are used for various functions, such as creating an operational overlay and analyzing information, and AI can be leveraged for these functions. However, tactical mobile edge devices have insufficient computing resources, which limits their ability to simultaneously perform various applications such as service-proven command and control, intelligence analysis, and fire operations, as well as applications using existing AI models. Therefore, a robust bare-bone convolutional neural network (CNN) model that can support reliable services on tactical mobile edge devices was proposed. The proposed model uses only four convolutional layers, has a performance equivalent to or better than that of existing CNN models, and has a stable and sufficient performance potential to perform multiple applications simultaneously. For experimental validation of the proposed model, a military symbol inferencer using self-collected handwritten military symbol images was implemented. This inferencer has an average accuracy of 95.42% when used alone, with CPU utilization reduced by up to 31.3% and inference time reduced by up to 47.2%. When running multiple applications in parallel, CPU utilization was reduced by up to 23.7% and inference time by up to 55.9%.

Abstract Specimen-induced aberration has been a major factor limiting the imaging depth of single-molecule localization microscopy (SMLM). Here, we report the application of label-free wavefront sensing adaptive optics to SMLM for deep-tissue super-resolution imaging. The proposed system measures complex tissue aberrations from intrinsic reflectance rather than fluorescence emission and physically corrects the wavefront distortion more than three-fold stronger than the previous limit. This enables us to resolve sub-diffraction morphologies of cilia and oligodendrocytes in whole zebrafish as well as dendritic spines in thick mouse brain tissues at the depth of up to 102 μm with localization number enhancement by up to 37 times and localization precision comparable to aberration-free samples. The proposed approach can expand the application range of SMLM to whole zebrafish that cause the loss of localization number owing to severe tissue aberrations.

Since the leakage of sodium in an SFR (sodium-cooled fast reactor) causes an explosion upon reaction with air and water, sodium leakages represent an important safety issue. In this study, a novel technique for improving the reliability of sodium leakage detection applying DDVR (dynamic data validation and reconciliation) is proposed and verified to resolve this technical issue. DDVR is an approach that aims to improve the accuracy of a target system in a dynamic state by minimizing random errors, such as from the uncertainty of instruments and the surrounding environment, and by eliminating gross errors, such as instrument failure, miscalibration, or aging, using the spatial redundancy of measurements in a physical model and the reliability information of the instruments. DDVR also makes it possible to estimate the state of unmeasured points. To validate this approach for supporting sodium leakage detection, this study applies experimental data from a sodium leakage detection experiment performed by the Korea Atomic Energy Research Institute. The validation results show that the reliability of sodium leakage detection is improved by cooperation between DDVR and hardware measurements. Based on these findings, technology integrating software and hardware approaches is suggested to improve the reliability of sodium leakage detection by presenting the expected true state of the system.

Abstract Optical imaging has been essential for scientific observations to date, however its biomedical applications has been restricted due to its poor penetration through tissues. In living tissue, signal attenuation and limited imaging depth caused by the wave distortion occur because of scattering and absorption of light by various molecules including hemoglobin, pigments, and water. To overcome this, methodologies have been proposed in the various fields, which can be mainly categorized into two stategies: developing new imaging probes and optical techniques. For example, imaging probes with long wavelength like NIR-II region are advantageous in tissue penetration. Bioluminescence and chemiluminescence can generate light without excitation, minimizing background signals. Afterglow imaging also has high a signal-to-background ratio because excitation light is off during imaging. Methodologies of adaptive optics (AO) and studies of complex media have been established and have produced various techniques such as direct wavefront sensing to rapidly measure and correct the wave distortion and indirect wavefront sensing involving modal and zonal methods to correct complex aberrations. Matrix-based approaches have been used to correct the high-order optical modes by numerical post-processing without any hardware feedback. These newly developed imaging probes and optical techniques enable successful optical imaging through deep tissue. In this review, we discuss recent advances for multi-scale optical imaging within deep tissue, which can provide reseachers multi-disciplinary understanding and broad perspectives in diverse fields including biophotonics for the purpose of translational medicine and convergence science. Graphical Abstract Methodologies for multi-scale optical imaging within deep tissues are discussed in diverse fields including biophotonics for the purpose of translational medicine and convergence science. Recent imaging probes have tried deep tissue imaging by NIR-II imaging, bioluminescence, chemiluminescence, and afterglow imaging. Optical techniques including direct/indirect and coherence-gated wavefront sensing also can increase imaging depth.

Monolayer transition metal dichalcogenides, due to a lack of inversion symmetry and strong spin orbit coupling, exhibit a number of unique features applicable to optical devices. Here, the authors use polarised microphotoluminescence to demonstrate optical pumping of the valley degree of freedom for charge-tunable MoS2 monolayers, the optical features of which they attribute to its unusual band structure.

Background and Objectives: The fruit of Schisandra chinensis (Turcz.) Baill. is widely used medicinally to treat coughs, asthma, exhaustion, eczema, and pruritus in Northeast Asian countries, including Korea, China, and Japan. This study was designed to investigate the effects of S. chinensis on dermatitis in mice with calcipotriol (MC-903)-induced atopic dermatitis (AD), and its effects on skin barrier dysfunction was also investigated. Materials and Methods: The inhibitory effects of an ethanolic extract of S. chinensis (EESC) on skin lesions, water content, water-holding capacity (WHC), histopathological abnormalities, and inflammatory cytokine and chemokine levels were evaluated in mice with AD induced by MC903. Results: Topical EESC ameliorated skin lesions, reduced skin water content, and increased MC903-induced WHC. EESC also prevented MC-903-induced histopathological abnormalities such as epidermal disruption, hyperkeratosis, spongiotic changes, and immune cell infiltration in inflamed tissue. Moreover, topical EESC reduced MC-903-induced levels of pro-inflammatory cytokines and chemokines, such as tumor necrosis factor (TNF)-α, interleukin (IL)-1β, IL-4, IL-6, IL-8, monocyte chemotactic protein (MCP)-1, and thymic stromal lymphopoietin (TSLP). Furthermore, unlike dexamethasone, EESC did not reduce the spleen/body weight ratio. Conclusions: These results suggest that S. chinensis can be used as an alternative to external corticosteroids and that its anti-inflammatory and skin barrier dysfunction-restoring effects are related to the downregulation of pro-inflammatory cytokines and chemokines, such as TNF-α, IL-4, IL-6, IL-8, and TSLP.

Optical excitation of transition metal dichalcogenide monolayers mostly generates excitons species with inherently short lifetime and spin/valley relaxation time. Here, the authors demonstrate efficient spin/valley optical pumping of resident electrons in n-doped WSe2 and WS2 monolayers.

The error-correction code proof-of-work (ECCPoW) algorithm is based on a low-density parity-check (LDPC) code. ECCPoW can impede the advent of mining application-specific integrated circuits (ASICs) with its time-varying puzzle generation capability. Previous research studies on ECCPoW algorithm have presented its theory and implementation on Bitcoin. In this study, we have not only designed ECCPoW for Ethereum, called ETH-ECC, but have also implemented, simulated, and validated it. In the implementation, we have explained how ECCPoW algorithm has been integrated into Ethereum 1.0 as a new consensus algorithm. Furthermore, we have devised and implemented a new method for controlling the difficulty level in ETH-ECC. In the simulation, we have tested the performance of ETH-ECC using a large number of node tests and demonstrated that the ECCPoW Ethereum works well with automatic difficulty-level change capability in real-world experimental settings. In addition, we discuss how stable the block generation time (BGT) of ETH-ECC is. Specifically, one key issue we intend to investigate is the finiteness of the mean of ETH-ECC BGT. Owing to a time-varying cryptographic puzzle generation system in ECCPoW algorithm, BGT in the algorithm may lead to a long-tailed distribution. Thus, simulation tests have been performed to determine whether BGT distribution is not heavy-tailed and has a finite mean. If the distribution is heavy-tailed, stable transaction confirmation cannot be guaranteed. In the validation, we have presented statistical analysis results based on the two-sample Anderson–Darling test and discussed how the BGT distribution follows an exponential distribution which has a finite mean. Our implementation is available for download at https://github.com/cryptoecc/ETH-ECC.

We propose a novel data-driven feature extraction approach based on direct causality and fuzzy temporal windows (FTWs) to improve the precision of human activity recognition and mitigate the problems of easily-confused activities and unlabeled data, which significantly degrade classification performance owing to the correlation of labeled data. In recognizing activities, the proposed approach not only considers the importance of oncoming short-term sensor data but also considers the continuity from past activities of the preceding long-term sensor data. In terms of the oncoming data, the causality feature is extracted using the direct transfer entropy to determine the unique pattern of an activity, which represents the quantified causal relationship between sensor activations. In terms of the preceding data, several hours of historical data are compressed to fuzzy features based on FTWs. Subsequently, the causality and fuzzy features are fused by matrix multiplication to express distinct features of activities. To effectively learn the spatiotemporal dependencies of the fused feature, deep long short-term memory (LSTM), two-dimensional convolutional neural network (2D-CNN), and hybrid models composed of a combination of LSTM and CNN were used. Leave-one-day-out cross-validation was performed based on the CASAS open datasets, including Aruba, Cairo, and Milan. The results showed that the macro-F1-scores were improved by 16.4, 37.5, and 18.5%, respectively, compared with those of the FTW-only environments. In addition, the proposed approach could improve the precision of activity recognition and mitigate the problems associated with the environments containing unlabeled data.

As the frequency of extreme weather events rises, the resilience of power systems is becoming increasingly important. This paper proposes a proactive microgrid management strategy for enhancing the resilience of microgrids (MGs) based on nested Mixed Integer Linear Programming problems with chance constraints. In the proposed method, MGs operate in a special operating mode referred to as the “preparation mode” to protect the vital load and maximize resource operation efficiency when an external grid outage warning is issued. The preparation mode problem is formulated to reflect both the normal and emergency mode operation conditions. The on-event phase-operation problem under emergency-mode operation conditions is nested within the pre-scheduling problem under normal-mode operation conditions in the preparation mode. Further, according to their importance, loads are divided into critical and non-critical ones in the problems. The former is represented by a chance constraint, and the latter is represented by the expected cost of load shedding in the cost function. The numerical examples demonstrate that the proposed preparation mode enables the MG to guarantee a high chance that the critical load will survive and to lower the cost of the non-critical load shedding with a minor increase in resource operation costs.

In this study, the drying shrinkage cracking characteristics of concrete containing 30% fly ash were investigated for various dune sand (DS) replacements and unit water contents. In the results of compressive strength, the mixture with a DS-to-fine aggregate (DS/FA) ratio of 10% showed the highest value, which was 17.6% higher than the lowest value. However, in the results of restrained drying shrinkage cracking, the mixtures with a DS/FA ratio of 20% showed the highest crack resistance, which was 24% higher than the lowest crack resistance. Therefore, it is necessary to consider DS replacement according to the required performance of concrete. The restraint effect factors of the aggregates were analyzed based on the relationship between the volume change of each aggregate and the change in the net time to cracking. The restraint effect factor of crushed sand was 0.71–0.98 and that of DS was 0.56–0.90 when that of the coarse aggregate (CA) was 1.

Abstract Recent development of FRET-PAINT microscopy significantly improved the imaging speed of DNA-PAINT, the previously reported super-resolution fluorescence microscopy with no photobleaching problem. Here we try to achieve the ultimate speed limit of FRET-PAINT by optimizing the camera speed, dissociation rate of DNA probes, and bleed-through of the donor signal to the acceptor channel, and further increase the imaging speed of FRET-PAINT by 8-fold. Super-resolution imaging of COS-7 microtubules shows that high-quality 40-nm resolution images can be obtained in just tens of seconds.

Abstract Three-dimensional (3D) reconstruction of thick samples using superresolution fluorescence microscopy remains challenging due to high level of background noise and fast photobleaching of fluorescence probes. We develop superresolution fluorescence microscopy that can reconstruct 3D structures of thick samples with both high localization accuracy and no photobleaching problem. The background noise is reduced by optically sectioning the sample using line-scan confocal microscopy, and the photobleaching problem is overcome by using the DNA-PAINT (Point Accumulation for Imaging in Nanoscale Topography). As demonstrations, we take 3D superresolution images of microtubules of a whole cell, and two-color 3D images of microtubules and mitochondria. We also present superresolution images of chemical synapse of a mouse brain section at different z-positions ranging from 0 μm to 100 μm.

It has been recently shown that the $l_{0}$ -norm problem can be reformulated into a mixed integer quadratic programming (MIQP) problem. CPLEX, a commercial optimization software package that can solve integer programming problems, is used to find the global solution to this MIQP problem for sparse signal estimation. However, CPLEX uses an exhaustive approach to search a feasible space to this MIQP problem. Thus, its running time grows exponentially as the problem dimension grows. This means that CPLEX quickly becomes computationally intractable for higher dimension problems. In this paper, we aim to propose a fast first-order-type method for solving this MIQP problem based on the alternating direction method. We conduct extensive simulations to demonstrate that: 1) our method is used to estimate a sparse signal by solving this problem and 2) our method is computationally tractable for problem dimensions up to the order of 1 million.

The Youngsan River estuary, located on the southwest coast of South Korea, has transitioned from a natural to an artificial estuary since dike construction in 1981 separated freshwater and seawater zones. This artificial transition has induced changes in the physical properties and circulation within the estuary, which has led to hypoxia and algal blooms. In this study, an artificial neural network (ANN) model was employed to simulate phytoplankton variations, including algal blooms and size fractions based on chlorophyll a, using data obtained by long-term monitoring (2008–2018) of the seawater zone of the Youngsan River estuary. The model was validated through statistical analyses, and the validated model was used to determine the contribution of the environmental factors on size-fractionated phytoplankton variations. The statistical validation of the model showed extremely low sum square error (SSE ≤ 0.0003) and root mean square error (RMSE ≤ 0.0173) values, with R2 ≥ 0.9952. The accuracy of the model predictions was high, despite the considerable irregularity and wide range of phytoplankton variations in the estuary. With respect to phytoplankton size structure, the contribution of seasonal environmental factors such as water temperature and solar radiation was high for net-sized chlorophyll a, whereas the contribution of factors such as freshwater discharge and salinity was high for nano-sized chlorophyll a, which includes typical harmful algae. Notably, because the Youngsan River estuary is influenced by a monsoon climate—characterized by high precipitation in summer—the contribution of freshwater discharge to harmful algal blooms is predicted to increase during this period. Our results suggest that the ANN model can be an important tool for understanding the influence of freshwater discharge, which is essential for managing algal blooms and maintaining the ecosystem health of altered estuaries.

Abstract Super-resolution fluorescence microscopy in the current form is hard to be used to image the neural connectivity of thick tissue samples due to problems such as slow imaging speed, severe photobleaching of fluorescent probes, and high background noise. Recently developed DNA-PAINT solved the photobleaching problem, but its imaging speed is extremely low. We report accelerated super-resolution fluorescence microscopy named FRET-PAINT. Compared to conventional DNA-PAINT, the imaging speed of the microscopy increases up to ~30-fold. As demonstrations, we show that 25-50 second imaging time is long enough to provide super-resolution reconstruction of microtubules and mitochondria of COS-7 cells.

In several wireless communication systems, robustness against jammers and covertness against eavesdroppers are required simultaneously. In this paper, we propose a novel covert anti-jamming communication system. The properties of both anti-jamming and covertness are achieved through the Gaussian-coded time-frequency modulation scheme. We propose two receiver algorithms based on the sparse signal recovery framework. The receiver algorithms estimate and remove the jamming signal from the received signal. In addition, it is difficult to distinguish the proposed signal from the actual Gaussian noise in both the time and frequency domains. We compare the covertness of the proposed communication system with that of a conventional digital modulation system in terms of the probability of detection. We numerically evaluated the bit error rate of the proposed system to demonstrate its anti-jamming performance.

In this study, the compressive strengths of concrete were investigated based on water content and aggregate volume fractions, comprising dune sand (DS), crushed sand (CS), and coarse aggregate (CA), for different ages. Experimental data were used to analyze the effects of the volume fraction changes of aggregates on the compressive strength. The compressive strength of concrete increases until the volumetric DS to fine aggregate (FA) ratio (DS/FA ratio) reaches 20%, after which it decreases. The relationship between changes in compressive strength and aggregate volume fractions was analyzed considering the effect factor of each aggregate on the compressive strength and at 2 conditions: (1) 0 < DS < CS < CA and (2) 0 < CA < CS < DS. For condition (1), when the effect factor of CA = 1, those of DS and CS were within 0.04–0.83 and 0.72–0.92, respectively, for all mixtures. For condition (2), when the effect factor of DS = 1, those of CS and CA were within 0.68–0.80 and 0.02–0.79, respectively.

The mechanism underlying neuroinflammation in X-linked adrenoleukodystrophy (ALD) is poorly understood. Here authors identify aberrant production of 25-hydroxycholesterol (25-HC) in ALD patient-derived cells, and show that 25-HC mediates neuroinflammation via activating the NLRP3 inflammasome.

This paper introduces the development of a transient monitoring system to detect the early stage of a transient, to identify the type of the transient scenario, and to inform an operator with the remaining time to turbine trip when there is no operator's relevant control. This study focused on the transients originating from a secondary system in nuclear power plants (NPPs), because the secondary system was recognized to be a more dominant factor to make unplanned turbine-generator trips which can ultimately result in reactor trips. In order to make the proposed methodology practical forward, all the transient scenarios registered in a simulator of a 1,000 MWe pressurized water reactor were archived in the transient pattern database. The transient patterns show plant behavior until turbine-generator trip when there is no operator's intervention. Meanwhile, the operating data periodically captured from a plant computer is compared with an individual transient pattern in the database and a highly matched section among the transient patterns enables isolation of the type of transient and prediction of the expected remaining time to trip. The transient pattern database consists of hundreds of variables, so it is difficult to speedily compare patterns and to draw a conclusion in a timely manner. The transient pattern database and the operating data are, therefore, converted into a smaller dimension using the principal component analysis (PCA). This paper describes the process of constructing the transient pattern database, dealing with principal components, and optimizing similarity measures.

Battery electric vehicle (BEV) sales have significantly increased in recent years. They have different energy consumption patterns compared to the fuel consumption patterns of internal combustion engine vehicles (ICEVs). This study quantified the impact of intersection control approaches—roundabout, traffic signal, and two-way stop controls—on BEVs’ energy consumption. The paper systematically investigates BEVs’ energy consumption patterns compared to the fuel consumption of ICEVs. The results indicate that BEVs’ energy consumption patterns are significantly different than ICEVs’ patterns. For example, for BEVs approaching a high-speed intersection, the roundabout was found to be the most energy-efficient intersection control, while the two-way stop sign was the least efficient. In contrast, for ICEVs, the two-way stop sign was the most fuel-efficient control, while the roundabout was the least efficient. Findings also indicate that the energy saving of traffic signal coordination was less significant for BEVs compared to the fuel consumption of ICEVs since more regenerative energy is produced when partial or poorly coordinated signal plans are implemented. The study confirms that BEV regenerative energy is a major factor in energy efficiency, and that BEVs recover different amounts of energy in different urban driving environments. The study suggests that new transportation facilities and control strategies should be designed to enhance BEVs’ energy efficiency, particularly in zero emission zones.

A power-based vehicle fuel consumption model, entitled the Virginia Tech Comprehensive Power-based Fuel Consumption Model (VT-CPFM) that was developed in an earlier publication is validated against in-field fuel consumption measurements. The study demonstrates that the VT-CPFMs calibrated using the EPA city and highway fuel economy ratings generally provide reliable fuel consumption estimates with a coefficient of determination in the range of 0.96. More importantly, both estimates and measurements produce very similar behavioral changes depending on engine load conditions. The VT-CPFMs are demonstrated to be easily calibrated using publically available data without the need to gather in-field instantaneous data.

The paper presents the results of a field experiment that was designed to compare manual driving, conventional cruise control (CCC) driving, and Eco-cruise control (ECC) driving with regard to fuel economy. The field experiment was conducted on five test vehicles along a section of Interstate 81 that was comprised of ±4% uphill and downhill grade sections. Using an Onboard Diagnostic II reader, instantaneous fuel consumption rates and other driving parameters were collected with and without the CCC system enabled. The collected data were compared with regard to fuel economy, throttle control, and travel time. The results demonstrate that CCC enhances vehicle fuel economy by 3.3 percent on average relative to manual driving, however this difference was not found to be statistically significant at a 5 percent significance level. The results demonstrate that CCC driving is more efficient on downhill versus uphill sections. In addition, the study demonstrates that an ECC system can produce fuel savings ranging between 8 and 16 percent with increases in travel times ranging between 3 and 6 percent. These benefits appear to be largest for heavier vehicles (SUVs).

The research identifies the steady-state car-following model parameters within state-of-the-practice traffic simulation software that require calibration to reflect inclement weather and roadway conditions. The research then develops procedures for calibrating non-steady state car-following models to capture inclement weather impacts and applies the procedures to the INTEGRATION software on a sample network. The results demonstrate that the introduction of rain precipitation results in a 5% reduction in light-duty vehicle speeds and a 3% reduction in heavy-duty vehicle speeds. An increase in the rain intensity further reduces light-duty vehicle and heavy-duty truck speeds resulting in a maximum reduction of 9.5% and 5.5% at the maximum rain intensity of 1.5 cm/h, respectively. The results also demonstrate that the impact of rain on traffic stream speed increases with the level of congestion and is more significant than speed differences attributed to various traffic operational improvements and thus should be accounted for in the analysis of alternatives. In the case of snow precipitation, the speed reductions are much more significant (in the range of 55%). Furthermore, the speed reductions are minimally impacted by the snow precipitation intensity. The study further demonstrates that precipitation intensity has no impact on the relative merit of various scenarios (i.e. the ranking of the scenario results are consistent across the various rain intensity levels). This finding is important given that it demonstrates that a recommendation on the optimal scenario is not impacted by the weather conditions that are considered in the analysis.

Exhaust gas recirculation (EGR) and high-pressure fuel injection are key technologies for reducing diesel engine emissions in the face of reinforced regulations. With the increasing need for advanced EGR technologies to achieve low-temperature combustion and low emission, the adverse etableffects of EGR must be addressed. One of the main problems is fouling of the EGR cooler, which involves the deposition of particulate matter (PM) due to the thermophoretic force between the cooler wall and flow field. A large amount of deposited PM can reduce the effectiveness of the heat exchanger in the EGR cooler and the de-NOx efficiency. In the present study, the effects of the variables that affect EGR cooler fouling are investigated by a comparison of laboratory-based and engine-based experiments. In the laboratory experiment, a soot generator that could readily provide separate control of the variables was used to generate the model EGR gas. Through control of the soot generator, it was possible to perform a parametric study by varying the particle size, the EGR gas flow rate, and the coolant temperature as the dominant parameters. A decrease in these factors caused an increase in the mass of the deposit and a drop in the effectiveness of the heat exchanger, related to fouling of the EGR cooler. In the engine-based experiment, engine-like conditions were provided to analyze real exhaust gas without a soot generator. Different variables were found to induce fouling of the EGR cooler, and the results of the engine-based experiment differed from those of the laboratory experiment. For example, in the engine-based experiment, a decrease in the EGR gas flow rate did not lead to a more pronounced drop in the effectiveness of the heat exchanger because of the increase in the concentration of PM in the EGR gas. This result shows that the conditions of the engine exhaust gas are different from those of the soot generator.

In this study, a simple, efficient, and economical process is reported for the direct synthesis of carbon nanotube (CNT) field emitters on metal alloy. Given that CNT field emitters can be customized with ease for compact and cold field emission devices, they are promising replacements for thermionic emitters in widely accessible X-ray source electron guns. High performance CNT emitter samples were prepared in optimized plasma conditions through the plasma-enhanced chemical vapor deposition (PECVD) process and subsequently characterized by using a scanning electron microscope, tunneling electron microscope, and Raman spectroscopy. For the cathode current, field emission (FE) characteristics with respective turn on (1 μA/cm2) and threshold (1 mA/cm2) field of 2.84 and 4.05 V/μm were obtained. For a field of 5.24 V/μm, maximum current density of 7 mA/cm2 was achieved and a field enhancement factor β of 2838 was calculated. In addition, the CNT emitters sustained a current density of 6.7 mA/cm2 for 420 min under a field of 5.2 V/μm, confirming good operational stability. Finally, an X-ray generated image of an integrated circuit was taken using the compact field emission device developed herein.