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Purpose Tracheostomy is a procedure which requires careful selection of tracheostomy tube size, because it can significantly impact patient outcomes. However, in situations where radiological imaging is unavailable for measuring the tracheal inner diameter (ID), it can be estimated using the patient’s height, weight, and sex. This study aimed to develop a method for estimating tracheal ID. Methods A retrospective study was conducted on 468 adult patients who underwent chest computed tomography and chest X-ray at the National Medical Center from 2019 to 2021. Tracheal ID at the level of the jugular notch was measured and cross-checked. The correlation of the patient’s body size and sex was then checked with tracheal ID and a regression equation was obtained to estimate tracheal ID. Results Height showed the greatest correlation with tracheal ID, followed by either ideal body weight (IBW) or adjusted body weight (ABW). The regression equation to estimate tracheal ID was as follows: “Expected ID of the trachea (mm)” = [11.0781 + (1.9682 for Male or 1 for Female)] + [7.3767 × height (cm)] − {0.8022 × [√ IBW (kg) for healthy weight or ABW (kg) for obese]}. The equation was applied to determine appropriate tracheostomy tube sizes. Conclusion Tracheal ID can be estimated using patient sex, height, and either IBW or ABW. By providing a practical method for estimating tracheal ID, the derived regression equation can serve as a valuable tool for healthcare professionals in emergency situations, which may reduce tracheostomy complication rates and deliver better patient outcomes.

To effectively manage the escalating traffic, attention is drawn to the coexistence mechanism of cellular and Wi-Fi networks which offload the cellular traffic from the licensed band to the unlicensed band. For the coexistence of 5G new radio-unlicensed and Wi-Fi, we tackle the energy efficiency maximization problem by sequentially determining the duty cycle and transmit power through the use of distributed deep Q-network (DQN) techniques. When utilizing the existing unlicensed band, the proposed method minimizes the impact on Wi-Fi networks while achieving optimal energy efficiency. Simulations validate the superior energy efficiency of the proposed coexistence mechanism over various benchmark methods.

Abstract The energy devices for generation, conversion, and storage of electricity are widely used across diverse aspects of human life and various industry. Three-dimensional (3D) printing has emerged as a promising technology for the fabrication of energy devices due to its unique capability of manufacturing complex shapes across different length scales. 3D-printed energy devices can have intricate 3D structures for significant performance enhancement, which are otherwise impossible to achieve through conventional manufacturing methods. Furthermore, recent progress has witnessed that 3D-printed energy devices with micro-lattice structures surpass their bulk counterparts in terms of mechanical properties as well as electrical performances. While existing literature focuses mostly on specific aspects of individual printed energy devices, a brief overview collectively covering the wide landscape of energy applications is lacking. This review provides a concise summary of recent advancements of 3D-printed energy devices. We classify these devices into three functional categories; generation, conversion, and storage of energy, offering insight on the recent progress within each category. Furthermore, current challenges and future prospects associated with 3D-printed energy devices are discussed, emphasizing their potential to advance sustainable energy solutions.

Abstract Additive manufacturing, or 3D printing attracts growing attention as a promising method for creating functionally graded materials. Fused deposition modeling (FDM) is widely available, but due to its simple process, creating spatial gradation of diverse properties using FDM is challenging. Here, we present a 3D printed digital material filament that is structured towards 3D printing of functional gradients, utilizing only a readily available FDM printer and filaments. The DM filament consists of multiple base materials combined with specific concentrations and distributions, which are FDM printed. When the DM filament is supplied to the same printer, its constituent materials are homogeneously blended during extrusion, resulting in the desired properties in the final structure. This enables spatial programming of material properties in extreme variations, including mechanical strength, electrical conductivity, and color, which are otherwise impossible to achieve with traditional FDMs. Our approach can be readily adopted to any standard FDM printer, enabling low-cost production of functional gradients.

Abstract Phase diagrams of materials are typically based on a static order parameter, but it faces challenges when distinguishing subtle phase changes, such as re-ordering. Here, we report a dynamic nonequilibrium order parameter termed re-order parameter to determine subtle phases and their transitions in interacting magnets. The dynamical precession of magnetization, so-called magnon, premises as a reliable re-order parameter of strong spin-orbit coupled magnets. We employ orthoferrites YFeO3 and its Mn-doped variations, where diverse magnetic phases, including canted antiferromagnetic (Γ4) and collinear antiferromagnetic (Γ1) states, have been well-established. Low-energy magnon uncovers the spin-orbit coupling-induced subtle magnetic structures, resulting in distinct terahertz emissions. The temporal and spectral parameters of magnon emission exhibit characteristics akin to BCS-type order parameters, constructing the magnetic phase diagram of Mn-doped YFeO3. This approach further reveals a concealed ferrimagnetic phase within the Γ1 state, underscoring its potential to search for hidden phases of materials, completing their phase diagrams.

In today’s rapidly evolving digital landscape, ensuring data integrity is paramount for maintaining the security and reliability of applications. This paper introduces the Application Integrity Assurance System (AIAS), a novel solution designed to enhance data integrity through the integration of Ethereum blockchain technology. AIAS leverages smart contracts and the Interplanetary File System (IPFS) to securely store and verify application manifests. By decentralizing the integrity assurance process, AIAS mitigates the risks associated with tampering and unauthorized modifications, providing a robust framework for maintaining data integrity in various application environments. The system has been prototyped and tested on an augmented reality platform, demonstrating its practical application and efficiency. The AIAS framework offers a cost-effective, infrastructure-free solution for safeguarding application integrity, making it an essential tool for platforms that demand high standards of data integrity and security.

Significant challenges are involved in tactical flying ad-hoc network (FANET) missions because network environments are very dynamic. In addition, energy-efficient network operation is important in tactical FANETs owing to the limited capacity of the on-board battery in unmanned aerial vehicles (UAVs). In a slotted-ALOHA (S-ALOHA)-based tactical FANET, frequent packet collisions due to changes in the network environment deteriorate the energy efficiency. Therefore, accurately estimating the number of active UAVs is crucial for improving the performance of S-ALOHA-based networks. Several estimation methods such as low-bound, Schoute, max-probability, and Bayesian estimation have been studied, and these methods perform well in static network environments; however, the estimation error significantly increases in dynamic network environments. To accurately estimate the number of active UAVs in highly dynamic environments, this study proposes an online hybrid pseudo-Bayesian estimation (HyPE) method. Specifically, this method combines the pure-Bayesian and pseudo-Bayesian estimation methods to overcome their shortages such as the inability in a dynamic environment of the pure-Bayesian method and the low estimation accuracy of the pseudo-Bayesian method. This paper compares the performance of the proposed HyPE method with that of benchmark methods in terms of the estimation error according to the variation period and variation step size. The results show that HyPE is more adaptable to dynamic changes in network environments.

Recently, multi-hop flying ad hoc networks (FANETs) have received considerable attention because of their various advantages, such as ease of deployment, scalability, cost, and latency reduction. However, FANETs still encounter serious problems such as a highly dynamic network topology and the limited on-board battery of unmanned aerial vehicles (UAVs). Thus, to increase the coverage probability while reducing unnecessary packet transmissions, realizing efficient and effective relay selection is important. To resolve these issues, this paper proposes a proactive and probabilistic uncovered neighbor-aware relay selection (P2URE) method that considers FANET’s topological dynamics. Specifically, the relay decision factor to identify relay UAV candidates and the probabilistic relay decision to reduce coverage overlaps and minimize redundant packet transmissions are devised. Through extensive simulations, it has been validated that the proposed P2URE method surpasses the benchmark methods in terms of the total number of transmissions, coverage probability, and energy efficiency. This superiority is observed across various network dynamics resulting from variations in the UAV density, speed, neighbor-information update period, and delay interval.

Abstract The Yellow Sea is one of the world’s most abundant marine resources, providing food and economic benefits to the Korean and Chinese populations. In spring 2020, a decrease in the intensity of phytoplankton bloom was observed. While one study attributed this decline to a decrease in nutrient associated with the COVID-19 pandemic, our previous research proposed weakened thermal stratification accompanied by a surface cooling anomaly as the cause. However, the relationship between the marine environment and ecosystem has not been fully elucidated. Using observations and marine physical-biogeochemical model data, we identified the weakened stratification as a critical factor for suppressing the 2020 spring bloom. Intense vertical mixing hindered the accumulation of nutrient and chlorophyll-a concentrations within the euphotic zone, resulting in a diminished phytoplankton bloom. In contrast, reduced nitrate and phosphate concentrations in 2020 were insignificant compared to those in 2017–2019, despite the notable decline in PM2.5 in March 2020 due to COVID-19. In April 2020, nutrient levels fell within the range of interannual variability based on long-term observations, reflecting a negligible effect on the spring phytoplankton bloom. Our findings provide insight into the importance of marine physical factors on the phytoplankton biomass in the Yellow Sea.

The spring phytoplankton bloom is a critical event in temperate oceans typically associated with the highest productivity levels throughout the year. To investigate the bloom process in the Yellow Sea, daily data on physical, chemical, and phytoplankton taxonomic group biomass, calculated via the chemotaxonomic approach, were collected from late March or early April to late May between 2018 and 2020 at the Socheongcho Ocean Research Station. During early spring (late March to mid-April), phytoplankton biomass increased, accompanied by a decrease in nutrient levels, with Bacillariophyceae and Cryptophyceae being the dominant groups. As water temperature increased, a pycnocline began to develop in late April, leading to a peak of the phytoplankton bloom dominated by chlorophytes and Cryptophyceae. Network analysis suggested that this phytoplankton bloom was caused by the onset of vertical stratification induced by increased sea surface temperature. The chlorophyte peak induced phosphate limitation above the pycnocline, resulting in succession to Prymnesiophyceae and Dinophyceae. Following pycnocline formation, phytoplankton biomass below the pycnocline was dominated by Bacillariophyceae and Cryptophyceae, with decreasing or fluctuating trends depending on phosphate concentration. Apart from these general patterns, 2019 and 2020 both had distinctive traits. The 2019 data revealed lower phosphate concentrations than the other 2 years, leading to a smaller chlorophyte peak at the surface compared to 2018 and extreme phosphate limitation above the pycnocline. This limitation resulted in decreased biomass of late successional groups, including Prymnesiophyceae and Dinophyceae. Pycnocline formation was delayed in year 2020, and stratification was significantly weaker compared to the previous 2 years. Due to the pycnocline delay, the surface chlorophyte peak did not develop and no succession to late successional groups was observed. Instead, high levels of Bacillariophyceae and Cryptophyceae biomass were observed throughout the water column with no surface bloom. Thus, among various environmental factors, increasing surface water temperature and phosphate concentrations play pivotal roles in shaping phytoplankton bloom dynamics. Distinct yearly variation points to the broader impacts of climate shifts, emphasizing the need for continued marine monitoring.

An integrated access and backhaul (IAB)-enabled small-cell network commonly utilizes frequency channels for access and backhaul links, and thus this network has a chance to utilize the frequency channels efficiently and optimally. However, there are still several problems with applying the IAB technology to practical small-cell networks, such as extremely high computational complexity caused by shared resource utilization and additional co-tier and cross-tier interference management. Therefore, we herein propose a multi-agent distributed Q-learning with pre-resource partitioning (MADQ-PRP) algorithm to solve the problem of frequency channel allocation and energy consumption. In MADQ-PRP, to reduce the computational complexity, each RL agent only considers its local state information to determine its following action. Nevertheless, by sharing and redistributing the rewards among agents, the overall reward can be maximized. Furthermore, we devise a pre-resource partitioning method depending on the variations in the number of SBSs per MBS and the numbers of MBS and SBS channels to reduce the computational complexity of the proposed MADQ-PRP algorithm. Through intensive simulations, we show the convergence of the proposed MADQ-PRP algorithm to the optimal solution obtained by the exhaustive search algorithm. Also, we demonstrate that the proposed MADQ-PRP algorithm outperforms several benchmark algorithms such as ‘Random action,’ ‘SBS on-off,’ ‘SBS-only,’ and ‘MADQ-only’ in IAB-enabled small-cell networks with non-uniform traffic distribution. Furthermore, it is confirmed that the proposed MADQ-PRP algorithm can reduce the CPU execution time by 9.1% and 97.9% compared to the distributed and centralized RL algorithms, respectively. The proposed algorithm based on the low-complexity RL and PRP could be one of the solutions to optimize the heterogeneous network performance from the perspective of the network operators when considering the coverage-capacity tradeoff.

In disasters, unmanned aerial vehicles (UAVs) can be used as aerial base stations (BSs) when terrestrial BSs are unavailable. Although most studies have focused on optimal UAV localization to provide efficient network connectivity for outdoor users, supporting indoor users is also of great importance. Additionally, fairly providing network connectivity to as many indoor users as possible is critical in emergencies. Therefore, we propose a table-based fair transmission algorithm based on carrier-sense multiple access with collision avoidance (CSMA/CA) that aims to provide as many equal opportunities as possible. Moreover, we propose a Lyapunov optimization-based optimal UAV positioning algorithm to satisfy various disaster requirements. To analyze the proposed algorithm’s performance, two building types were considered: standard- and factory-type buildings. We then compared the proposed table-based fair transmission algorithm with the conventional protocol according to various building types. Furthermore, via intensive simulations, we demonstrated the Lyapunov optimization-derived UAV movements according to situational requirement variations.

The need for high-throughput analysis of multiple analytes for inborn errors of metabolism in newborn screening (NBS) has led to the introduction of tandem mass spectrometry (MS/MS) into the NBS laboratory. In a flow-injection analysis (FIA), the predominant MS/MS method utilized for NBS, samples are introduced directly into the mass spectrometer without chromatographic separation. When a high-throughput FIA-based MS/MS method is implemented on newer generations of mass spectrometers with increased sensitivity, the risk of carryover and contamination increases. In the present study, we report the carryover of ornithine identified during the implementation of the NeoBase™ 2 (PerkinElmer) non-derivatized kits on the Xevo-TQD platform (Waters Corporation) and describe the source of the carryover, which was traced to the stainless-steel frit-type inline filter. Furthermore, a possible compound-dependent interaction with the stainless-steel frit is suggested based on the structure of ornithine and its effect on separation techniques. Investigation and mitigation of carryover can be a time and resource consuming process, and to this end, our report on identification of a stainless-steel frit as the source of delayed elution and carryover of ornithine should be recognized as a rare, albeit possible source of carryover in FIA-MS/MS methods adopted for NST.

In B5G heterogeneous cellular networks, a rapid increase in the number of small cell base stations (SBSs) to support a massive number of devices tends to waste a considerable amount of energy. Therefore, intelligent management of SBSs’ power consumption is one of the most important research issues. We herein propose quasi-distributed Q-learning-based cell breathing (QD-QCB) considering full and partial SBS collaborations for maximizing network energy efficiency. Also, the concept of an aggregated active SBS set based on regional user distributions is proposed for computing- and energy-efficient operation. Through intensive simulations, we show that the proposed QD-QCB algorithm can achieve optimal energy efficiency, and improve the network energy efficiency significantly compared with conventional algorithms such as no transmit power control, random cell breathing, and greedy cell breathing algorithms.

Here the authors present an active thermal control system using 4Dprinted shape memory polymers and demonstrate how distinct deformation mechanisms lead to unique, tunable thermal properties using stretching- and bending-dominated architectures. Infrared thermography measurements with varying temperature and compression settings show that at low strains, radiation drives the effective conductance increase as the view factors among the struts increase with increasing strain, and at higher strains, conduction drives the effective conductance increase as the strut-to-strut contact areas increase. The effective thermal conductance increases from 4.41mW/K to 14.52mW/K and from 3.23mW/K to 10.48mW/K for the Kelvin foam and octet-truss microlattices, respectively, as strain increases from 0% to approximately 70%. As the strain is adjusted, the stretching-dominated octet-truss architecture exhibits abrupt changes in shape and conductance due to buckling. The bending-dominated Kelvin foam architecture allows for gradual geometric changes and precise tuning of thermal conductance. These findings provide a new understanding of thermal transport phenomena in 4D-printed metamaterials, which may be a breakthrough in tunable thermal systems.

To overcome the problems caused by the limited battery lifetime in multiple-unmanned aerial vehicle (UAV) wireless networks, we propose a hierarchical multi-agent reinforcement learning (RL) framework to maximize the energy efficiency (EE) of UAVs by finding the optimal frequency reuse factor and transmit power. The proposed algorithm consists of distributed inner-loop RL for transmit power control of the UAV terminal (UT) and centralized outer-loop RL for finding the optimal frequency reuse factor. Specifically, the proposed algorithm adjusts these two factors jointly to effectively mitigate intercell interference and reduce undesired transmit power consumption in multi-UAV wireless networks. We show that, for this reason, the proposed algorithm outperforms conventional algorithms, such as a random action algorithm with a fixed frequency reuse factor and a hierarchical multi-agent Q-learning algorithm with binary transmit power controls. Furthermore, even in the environment where UTs are continuously moving based on the mixed mobility model, we show that the proposed algorithm can find the best reward when compared to conventional algorithms.

In this study, we investigate the use of low-altitude unmanned aerial vehicles (UAVs) in air-to-ground (A2G) communication networks, which still have various issues and challenges. In particular, the limited battery power of UAVs render their flight time extremely short. Hence, we present cooperative dynamic framed slotted-ALOHA (CDFS-ALOHA), which is capable of random transmit power control (TPC). When collisions occur, because UAVs can select their retransmit power randomly, their power consumption is reduced and their probability of successful packet transmission is improved owing to a capture effect. In terms of performance metrics, we consider the successful packet transmission probability, UAV power consumption, and network-wide energy efficiency. CDFS-ALOHA with TPC can directly reduce the transmit power consumption of UAVs compared to CDFS-ALOHA without TPC; thus, intra- and intercell interference can be reduced. Consequently, CDFS-ALOHA with TPC can obtain sporadic and intermittent capture effects, thus improving the network-wide energy efficiency without reducing the probability of successful packet transmission in A2G communication networks.

Recently, the demand for small cell base stations (SBSs) has been exploding to accommodate the explosive increase in mobile data traffic. In ultra-dense small cell networks (UDSCNs), because the spatial and temporal traffic distributions are significantly disproportionate, the efficient management of the energy consumption of SBSs is crucial. Therefore, we herein propose a multi-agent distributed Q-learning algorithm that maximizes energy efficiency (EE) while minimizing the number of outage users. Through intensive simulations, we demonstrate that the proposed algorithm outperforms conventional algorithms in terms of EE and the number of outage users. Even though the proposed reinforcement learning algorithm has significantly lower computational complexity than the centralized approach, it is shown that it can converge to the optimal solution.

Understanding of the primary production of phytoplankton in the Kara Sea (KS), the Laptev Sea (LS), and the East Siberian Sea (ESS) remains limited, despite the recognized importance of phytoplankton in the Arctic Ocean. To address this knowledge gap, we conducted three NABOS (Nansen and Amundsen Basins Observational System) expeditions in 2013, 2015, and 2018 to measure in situ primary production rates using a 13C-15N dual-tracer method and examine their major controlling factors. The main goals in this study were to investigate regional heterogeneity in primary production and derive its contemporary ranges in the KS, LS, and ESS. The daily primary production rates in this study (99 ± 62, 100 ± 77, and 56 ± 35 mg C m−2 d−1 in the KS, LS, and ESS, respectively) are rather different from the values previously reported in each sea mainly because of spatial and regional differences. Among the three seas, a significantly lower primary production rate was observed in the ESS in comparison to those in the KS and LS. This is likely mainly because of regional differences in freshwater content based on the noticeable relationship (Spearman, rs = −0.714, p < 0.05) between the freshwater content and the primary production rates observed in this study. The contemporary ranges of the annual primary production based on this and previous studies are 0.96–2.64, 0.72–50.52, and 1.68–16.68 g C m−2 in the KS, LS, and ESS, respectively. Further intensive field measurements are warranted to enhance our understanding of marine microorganisms and their community-level responses to the currently changing environmental conditions in these poorly studied regions of the Arctic Ocean.

Maragoni microswimmers show the advantage of self-propulsion but their development is limited by fabrication techniques. Here, the authors use a photopatterning method which allows for a high throughput production of maragoni microswimmers with multiple functional parts and distinct materials properties.

Shape memory polymers are gaining significant interest as one of the major constituent materials for the emerging field of 4D printing. While 3D-printed metamaterials with shape memory polymers show unique thermomechanical behaviors, their thermal transport properties have received relatively little attention. Here, we show that thermal transport in 3D-printed shape memory polymers strongly depends on the shape, solid volume fraction, and temperature and that thermal radiation plays a critical role. Our infrared thermography measurements reveal thermal transport mechanisms of shape memory polymers in varying shapes from bulk to octet-truss and Kelvin-foam microlattices with volume fractions of 4%–7% and over a temperature range of 30–130 °C. The thermal conductivity of bulk shape memory polymers increases from 0.24 to 0.31 W m−1 K−1 around the glass transition temperature, in which the primary mechanism is the phase-dependent change in thermal conduction. On the contrary, thermal radiation dominates heat transfer in microlattices and its contribution to the Kelvin-foam structure ranges from 68% to 83% and to the octet-truss structure ranges from 59% to 76% over the same temperature range. We attribute this significant role of thermal radiation to the unique combination of a high infrared emissivity and a high surface-to-volume ratio in the shape memory polymer microlattices. Our work also presents an effective medium approach to explain the experimental results and model thermal transport properties with varying shapes, volume fractions, and temperatures. These findings provide new insights into understanding thermal transport mechanisms in 4D-printed shape memory polymers and exploring the design space of thermomechanical metamaterials.

Abstract Leukapheresis is used for the mechanical removal of leukaemic cells in hyperleukocytosis. However, the effectiveness of leukapheresis remains unclear due to selection and confounding factors in the cohorts. We compared the effectiveness of leukapheresis among the subgroups according to either the 2016 World Health Organization classification or the number of cytogenetic abnormalities with a retrospective, single-centre study from January 2009 to December 2018. Acute myeloid leukaemia (AML, n = 212) and acute lymphoblastic leukaemia (ALL, n = 97) were included. The 30-day survival rates (95% confidence interval, 95% CI) for AML and ALL were 86.3% (81.6–90.9%) and 94.8% (90.3–99.2%), respectively. For AML, ‘primary AML with myelodysplasia-related changes’ and ‘AML with biallelic mutation of CEBPA’ showed better 30-day survival outcomes (P = 0.026) than the other subgroups. A higher platelet count after leukapheresis was associated with better 30-day survival in AML patients (P = 0.029). A decrease in blast percentage count after leukapheresis was associated with better 30-day survival in ALL patients (P = 0.034). Our study suggested that prophylactic platelet transfusion to raise the platelet count to 50 × 109/L or greater might improve clinical outcome in AML patients undergoing leukapheresis.

In this paper, to maximize the lifetime of drone swarms by resolving the battery exhaustion problems caused by undesired retransmissions, we propose a residual energy-aware online random access scheme (RE-ORA) by adjusting the packet transmission opportunities based on the residual energy of a drone in S-ALOHA-based swarming drone networks. We aim to improve the battery lifetime of the drone with the smallest energy among the drone swarms. In addition, we analyze the success, collision, and idle probabilities of the swarming drone networks. In particular, we analyze the intradrone swarm collision and interdrone swarm collision probabilities. Through intensive simulations, we show that the proposed RE-ORA scheme outperforms the conventional algorithm with respect to the average lifetime of drone swarms and successful packet transmission probability according to the number of drones and the residual energy.

Writing data in DNA is still a bottleneck due to the reliance on chemical synthesis methods. Here the authors report multiplexed enzymatic DNA synthesis using maskless photolithography.

Recently, there has been a rapid increase in the number of (small-cell) base stations (BSs) to support the massive amount of mobile data traffic and rapidly increasing number of mobile devices in beyond 5G (B5G) wireless communication systems or Internet of Things (IoT) networks. However, many of these BSs tend to waste a considerable amount of energy to support such data traffic and mobile devices. Therefore, the development of an efficient BS status control algorithm is important for realizing energy-efficient IoT networks. To reduce network energy consumption, we herein propose a density clustering-based BS control algorithm for energy-efficient IoT networks (DeCoNet). DBSCAN (Density-Based Spatial Clustering of Applications with Noise) and OPTICS (Ordering Points To Identify the Clustering Structure) are utilized for partitioning high and low user-density regions. To find the effective number of BSs and their appropriate locations considering user-density differences, we utilize parameters obtained after applying density clustering algorithms to derive the thinning radius that is used to adjust the status of BSs in overall cellular IoT networks. Specifically, the average reachability-distance of each cluster in OPTICS and the distance between the outermost border users of each cluster in DBSCAN are used to obtain the radius of each cluster region. Through extensive computer simulations, we show that the proposed algorithms outperform the conventional algorithms in terms of average area throughput, energy efficiency, energy per information bit, and power consumption per unit area.

Time-division multiple access (TDMA)-based medium access control (MAC) protocol has been widely used for avoiding access conflicts in wireless multi-hop ad hoc networks, where the time synchronization among wireless nodes is essential. In this paper, we propose a novel time synchronization protocol for TDMA-based cooperative multi-hop wireless ad hoc networks, which are also called barrage relay networks (BRNs). The proposed time synchronization protocol is based on cooperative relay transmissions to send time synchronization messages. We also propose a network time reference (NTR) selection technique for improving the convergence time and average time error. In the proposed NTR selection technique, each node overhears the user identifier (UID) of other nodes, hop count (HC) from them to itself, and network degree, which denotes the number of 1-hop neighbor nodes. Then, the node with the minimum HC from all other nodes is selected as the NTR node. If there are multiple nodes with the minimum HC, the node with the larger degree is selected as the NTR node. To the best of our knowledge, the proposed time synchronization protocol with the NTR selection is introduced for the first time for cooperative (barrage) relay networks in this paper. Through computer simulations, we validate the proposed time synchronization protocol in terms of the average time error under various practical network scenarios. Furthermore, we also compare the performance of the proposed protocol with the conventional time synchronization methods. It is shown that the proposed protocol significantly outperforms the conventional methods in terms of the average time error and convergence time. The proposed protocol is shown to be more robust against packet loss as well.

Laboratory-scale data on a component level are frequently used for prognostics because acquiring them is time and cost efficient. However, they do not reflect actual field conditions. As prognostics is for an in-service system, the developed prognostic methods must be validated using real operational data obtained from an actual system. Because obtaining real operational data is much more expensive than obtaining test-level data, studies employing field data are scarce. In this study, a prognostic method for screws was presented by employing multi-source real operational data obtained from a micro-extrusion system. The analysis of real operational data is more challenging than that of test-level data because the mutual effect of each component in the system is chaotically reflected in the former. This paper presents a degradation feature extraction method for interpreting complex signals for a real extrusion system based on the physical and mechanical properties of the system as well as operational data. The data were analyzed based on general physical properties and the inferred interpretation was verified using the data. The extracted feature exhibits valid degradation behavior and is used to predict the remaining useful life of the screw in a real extrusion system.

The CHEMTAX program has been widely used to estimate community composition based on major pigment concentrations in seawater. However, because CHEMTAX is an underdetermined optimization algorithm, underdetermined bias has remained an unsolved problem since its development in 1996. The risk of producing biased results increases when analyzing the picophytoplankton community; therefore, this study tested a new method for avoiding biased CHEMTAX results using the picophytoplankton community around the East Sea (Japan Sea). This method involves building a linear model between pigment concentration data and community composition data based on DNA sequencing to predict the pigment range for each operational taxonomic unit, based on the 95% prediction interval. Finally, the range data are transformed into an initial ratio and ratio limits for CHEMTAX analysis. Three combinations of initial ratios and ratio limits were tested to determine whether the modeled initial ratio and ratio limit could prevent underdetermined bias in the CHEMTAX estimates; these combinations were the modeled initial ratio and ratio limit, the modeled initial ratio with a default ratio limit of 500 s, and an initial ratio from previous research with the default ratio limit. The final ratio and composition data for each combination were compared with Bayesian compositional estimator-based final ratio and composition data, which are robust against underdetermined bias. Only CHEMTAX analysis using the modeled initial ratio and ratio limit was unbiased; all other combinations showed significant signs of bias. Therefore, the findings in this study indicate that ratio limits and the initial ratio are equally important in the CHEMTAX analysis of biased datasets. Moreover, we obtained statistically supported initial ratios and ratio limits through linear modeling of pigment concentrations and 16s rDNA composition data.

Summary: Glioblastoma is the most common and deadly primary brain malignancy. Despite advances in precision medicine oncology (PMO) allowing the identification of molecular vulnerabilities in glioblastoma, treatment options remain limited, and molecular assays guided by genomic and expression profiling to inform patient enrollment in life-saving trials are lacking. Here, we generate four-dimensional (4D) cell-culture arrays for rapid assessment of drug responses in glioblastoma patient-derived models. The arrays are 3D printed with thermo-responsive shape memory polymer (SMP). Upon heating, the SMP arrays self-transform in time from 3D cell-culture inserts into histological cassettes. We assess the utility of these arrays with glioblastoma cells, gliospheres, and patient derived organoid-like (PDO) models and demonstrate their use with glioblastoma PDOs for assessing drug sensitivity, on-target activity, and synergy in drug combinations. When including genomic and drug testing assays, this platform is poised to offer rapid functional drug assessments for future selection of therapies in PMO.

In this paper, we propose a new 3-D image transmission method using in-network computation (INC) for integral imaging. In integral imaging, information of 3-D objects can be recorded as multiple 2-D elemental images with different perspectives so that the amount of transmitted data is extremely large. Thus, the transmission efficiency for 3-D images via wireless networks is very low. To improve this transmission efficiency, we determine the correlation among elemental images and reduce the amount of data transmitted by INC, which allows the intermediate nodes in the network to compress the correlated images before transmission. Experimental results demonstrate that the proposed method does not degrade the visual quality of 3-D images greatly, but it reduces the amount of transmitted data significantly.

High-quality, single-crystal-like Cu2O thin films of various thicknesses (10 nm–45 nm) were prepared at a low temperature (150 °C) by controlling layer-by-layer oxidation of wafer-scale Cu thin films sputtered along the (111) direction using a pure single-crystal Cu target. The cross-sectional images of the thin films reveal high crystallinity of Cu2O layers except for 60° twinning in the sequential stacking order as evidenced by high-resolution transmission electron microscopy, which is consistent with the absence of the photoluminescence (PL) signals arising from atomic-scale vacancies. The optical properties of our Cu2O films were investigated using temperature-dependent PL and Raman spectroscopy. All of the Cu2O thin films exhibit characteristic band-to-band transitions together with the series of yellow excitonic transitions slightly below the fundamental bandgap. The spectral locations for the PL are approximately consistent with those for the bulk counterpart. The excellent optical quality of our Cu2O was further demonstrated by significantly reduced quasi-direct transition that occurs at symmetry-breaking crystal imperfection, which relaxes the stringent momentum conservation rule. We identified the three main Raman scattering modes of the Cu2O thin films, where the two forbidden modes of Γ15(1) and Γ12−+Γ25− are resonantly allowed by the proximity of the incident photon energy to the green bandgap. We believe that our synthesis technique can be utilized for the preparation of single-crystal-like metal oxide thin films at low production temperatures with precise thickness control for the development of novel optoelectronic devices and for the exploration of the nanoscale light-matter interaction as well.

Benthic diatoms isolated from tidal flats in the west coast of Korea were identified through both traditional morphological method and molecular phylogenetic method for methodological comparison. For the molecular phylogenetic analyses, we sequenced the 18S rRNA and the ribulose bisphosphate carboxylase large subunit coding gene, rbcL. Further, the comparative analysis allowed for the assessment of the suitability as a genetic marker for identification of closely related benthic diatom species and as potential barcode gene. Based on the traditional morphological identification system, the 61 isolated strains were classified into 52 previously known taxa from 13 genera. However, 17 strains could not be classified as known species by morphological analyses, suggesting a hidden diversity of benthic diatoms. The Blast search on NCBI's Genebank indicated that the reference sequences for most of the species were absent for the benthic diatoms. Of the two genetic markers, the rbcL genes were more divergent than the 18S rRNA genes. Furthermore, a long branch attraction artefact was found in the 18S rRNA phylogeny. These results suggest that the rbcL gene is a more appropriate genetic marker for identification and classification of benthic diatoms. Considering their high diversity and simple shapes, and thus the difficulty associated with morphological classification of benthic diatoms, a molecular approach could provide a relatively easy and reliable classification system. However, this study suggests that more effort should be made to construct a reliable database containing polyphasic taxonomic data for diatom classification.

Despite recent advances in additive manufacturing (AM) that shifts the paradigm of modern manufacturing by its fast, flexible, and affordable manufacturing method, the achievement of high-dimensional accuracy in AM to ensure product consistency and reliability is still an unmet challenge. This study suggests a general method to establish a mathematical spatial uncertainty model based on the measured geometry of AM microstructures. Spatial uncertainty is specified as the deviation between the planned and the actual AM geometries of a model structure, high-aspect-ratio struts. The detailed steps of quantifying spatial uncertainties in the AM geometry are as follows: (1) image segmentation to extract the sidewall profiles of AM geometry; (2) variability-based sampling; (3) Gaussian process modeling for spatial uncertainty. The modeled spatial uncertainty is superimposed in the CAD geometry and finite element analysis is performed to quantify its effect on the mechanical behavior of AM struts with different printing angles under compressive loading conditions. The results indicate that the stiffness of AM struts with spatial uncertainty is reduced to 70% of the stiffness of CAD geometry and the maximum von Mises stress under compressive loading is significantly increased by the spatial uncertainties. The proposed modeling framework enables the high fidelity of computer-based predictive tools by seamlessly incorporating spatial uncertainties from digital images of AM parts into a traditional finite element model. It can also be applied to parts produced by other manufacturing processes as well as other AM techniques.

Although additive manufacturing (AM) offers great potential to revolutionize modern manufacturing, its layer-by-layer process results in a staircase-like rough surface profile of the printed part, which degrades dimensional accuracy and often leads to a significant reduction in mechanical performance. In this paper, we present a systematic approach to improve the surface profile of AM parts using a computational model and a multi-objective optimization technique. A photopolymerization model for a micro 3D printing process, projection micro-stereolithography (PμSL), is implemented by using a commercial finite element solver (COMSOL Multiphysics software). First, the effect of various process parameters on the surface roughness of the printed part is analyzed using Taguchi’s method. Second, a metaheuristic optimization algorithm, called multi-objective particle swarm optimization, is employed to suggest the optimal PμSL process parameters (photo-initiator and photo-absorber concentrations, layer thickness, and curing time) that minimize two objectives; printing time and surface roughness. The result shows that the proposed optimization framework increases 18% of surface quality of the angled strut even at the fastest printing speed, and also reduces 50% of printing time while keeping the surface quality equal for the vertical strut, compared to the samples produced with non-optimized parameters. The systematic approach developed in this study significantly increase the efficiency of optimizing the printing parameters compared to the heuristic approach. It also helps to achieve 3D printed parts with high surface quality in various printing angles while minimizing printing time.

The medium access control protocol plays an important role to decrease access collisions in dense wireless sensor networks where multiple sensors in the same vicinity attempt to transmit a packet simultaneously. In this article, we propose a distributed medium access control protocol that uses successive multiple collision detection phases for dense wireless sensor network environments by enhancing the typical carrier sense multiple access with collision resolution protocol that uses only a single collision detection phase. In the proposed medium access control protocol, colliding stations are filtered in each collision detection phase and only surviving stations compete again in the next collision detection phase. Therefore, the collision detection probability becomes higher as the collision detection phases proceed. Utilizing the successive multiple collision detection phases, we analyze the throughput numerically and find optimal operating parameters—such as the number of collision detection phases and the number of collision detection slots per phase—that maximize the throughput. Analysis and simulation results show that the proposed medium access control protocol using the successive collision detection technique significantly outperforms the conventional carrier sense multiple access with collision resolution protocol.

In opportunistic device-to-device (D2D) networks, the epidemic routing protocol can be used to optimize the message delivery ratio. However, it has the disadvantage that it causes excessive coverage overlaps and wastes energy in message transmissions because devices are more likely to receive duplicates from neighbors. We therefore propose an efficient data dissemination algorithm that can reduce undesired transmission overlap with little performance degradation in the message delivery ratio. The proposed algorithm allows devices further away than the k-th furthest distance from the source device to forward a message to their neighbors. These relay devices are determined by analysis based on a binomial point process (BPP). Using a set of intensive simulations, we present the resulting network performances with respect to the total number of received messages, the forwarding efficiency and the actual number of relays. In particular, we find the optimal number of relays to achieve almost the same message delivery ratio as the epidemic routing protocol for a given network deployment. Furthermore, the proposed algorithm can achieve almost the same message delivery ratio as the epidemic routing protocol while improving the forwarding efficiency by over 103% when k≥10.

This study considers a ultra-dense network (UDN) in which the enormous number of base stations (BSs) are densely deployed to support the massive amount of data traffic generated by many mobile devices. In this paper, we propose an approximate thinning-based judicious network control algorithm for energy-efficient UDNs (ACEnet) to improve the area throughput while diminishing the network energy consumption. The main idea of the proposed ACEnet algorithm is to judiciously adjust the modes of the BSs according to active-user density based on the thinning operation in stochastic geometry framework. The stochastic geometry framework is exploited to analyze the performance of the proposed algorithm, which includes the signal-to-interference-plus-noise ratio (SINR), average achievable rate of users, area throughput, and energy efficiency. Through intensive simulations, it shows that the proposed algorithm outperforms conventional algorithms. We also demonstrate that the analytical results are well matched with the simulation results.

Improving energy efficiency is the most important challenge in wireless sensor networks. Because sensing information is correlated in many sensor network applications, some previous works have proposed ideas that reduce the energy consumption of the network by exploiting the spatial correlation between sensed information. In this paper, we propose a distributed data compression framework that exploits the broadcasting characteristic of the wireless medium to improve energy efficiency. We analyze the performance of the proposed framework numerically and compare it with the performance of previous works using simulation. The proposed scheme performs better when the sensing information is correlated.