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Abstract Stretchable optoelectronic devices are typically realized through a 2D integration of rigid components and elastic interconnectors to maintain device performance under stretching deformation. However, such configurations inevitably sacrifice the area ratio of active components to enhance the maximum interconnector strain. We herein propose a 3D buckled height-alternant architecture for stretchable OLEDs that enables the high active-area ratio and the enhanced maximum strain simultaneously. Along with the optimal dual serpentine structure leading to a low critical buckling strain, a pop-up assisting adhesion blocking layer is proposed based on an array of micro concave structures for spatially selective adhesion control, enabling a reliable transition to a 3D buckled state with OLED-compatible processes. Consequently, we demonstrate stretchable OLEDs with both the high initial active-area ratio of 85% and the system strain of up to 40%, which would require a lateral interconnector strain of up to 512% if it were attained with conventional 2D rigid-island approaches. These OLEDs are shown to exhibit reliable performance under 2,000 biaxial cycles of 40% system strain. 7 × 7 passive-matrix OLED displays with the similar level of the initial active-area ratio and maximum system strain are also demonstrated.

Abstract Demyelination is a common pathological feature in a wide range of diseases, characterized by the loss of myelin sheath and myelin-supporting oligodendrocytes. These losses lead to impaired axonal function, increased vulnerability of axons to damage, and result in significant brain atrophy and neuro-axonal degeneration. Multiple pathomolecular processes contribute to neuroinflammation, oligodendrocyte cell death, and progressive neuronal dysfunction. In this study, we use the cuprizone mouse model of demyelination to investigate long-term non-invasive gamma entrainment using sensory stimulation as a potential therapeutic intervention for promoting myelination and reducing neuroinflammation in male mice. Here, we show that multisensory gamma stimulation mitigates demyelination, promotes oligodendrogenesis, preserves functional integrity and synaptic plasticity, attenuates oligodendrocyte ferroptosis-induced cell death, and reduces brain inflammation. Thus, the protective effects of multisensory gamma stimulation on myelin and anti-neuroinflammatory properties support its potential as a therapeutic approach for demyelinating disorders.

Abstract Stretchable organic light-emitting diodes (OLEDs) have emerged as promising optoelectronic devices with exceptional degree of freedom in form factors. However, stretching OLEDs often results in a reduction in the geometrical fill factor (FF), that is the ratio of an active area to the total area, thereby limiting their potential for a broad range of applications. To overcome these challenges, we propose a three-dimensional (3D) architecture adopting a hidden active area that serves a dual role as both an emitting area and an interconnector. For this purpose, an ultrathin OLED is first attached to a 3D rigid island array structure through quadaxial stretching for precise, deformation-free alignment. A portion of the ultrathin OLED is concealed by letting it ‘fold in’ between the adjacent islands in the initial, non-stretched condition and gradually surfaces to the top upon stretching. This design enables the proposed stretchable OLEDs to exhibit a relatively high FF not only in the initial state but also after substantial deformation corresponding to a 30% biaxial system strain. Moreover, passive-matrix OLED displays that utilize this architecture are shown to be configurable for compensation of post-stretch resolution loss, demonstrating the efficacy of the proposed approach in realizing the full potential of stretchable OLEDs.

Abstract Human cytomegalovirus (CMV) is the most common infectious cause of complications post-transplantation, while a CMV vaccine for transplant recipients has yet to be licensed. Triplex, a multiantigen Modified Vaccinia Ankara (MVA)-vectored CMV vaccine candidate based on the immunodominant antigens phosphoprotein 65 (pp65) and immediate-early 1 and 2 (IE1/2), is in an advanced stage of clinical development. However, its limited genetic and expression stability restricts its potential for large-scale production. Using a recently developed fully synthetic MVA (sMVA) platform, we developed a new generation Triplex vaccine candidate, T10-F10, with different sequence modifications for enhanced vaccine stability. T10-F10 demonstrated genetic and expression stability during extensive virus passaging. In addition, we show that T10-F10 confers comparable immunogenicity to the original Triplex vaccine to elicit antigen-specific T cell responses in HLA-transgenic mice. These results demonstrate improvements in translational vaccine properties of an sMVA-based CMV vaccine candidate designed as a therapeutic treatment for transplant recipients.

Abstract Entangled coherent states play pivotal roles in various fields such as quantum computation, quantum communication, and quantum sensing. We experimentally demonstrate the generation of entangled coherent states with the two-dimensional motion of a trapped ion system. Using Raman transitions with appropriate detunings, we simultaneously drive the red and blue sidebands of the two transverse axes of a single trapped ion and observe multi-periodic entanglement and disentanglement of its spin and two-dimensional motion. Then, by measuring the spin state, we herald entangled coherent states of the transverse motions of the trapped ion and observe the corresponding modulation in the parity of the phonon distribution of one of the harmonic oscillators. Lastly, we trap two ions in a linear chain and realize Mølmer–Sørensen gate using two-dimensional motion.

Abstract Charge storage synaptic circuits employing InGaZnO thin‐film transistors (IGZO TFTs) and capacitors are a promising candidate for on‐chip trainable neural network hardware accelerators. However, IGZO TFTs often exhibit bias instability. For synaptic memory applications, the programming transistors are predominantly exposed to asymmetric off‐state biases, and a unique field‐dependent on‐current reduction under off‐scenario is observed which may result in programming current variation. Further examination of the phenomenon is conducted with transmission line‐like method and degradation recovery tests, and current reduction can be attributed to contact resistance increase by charge trapping in the source and drain electrode and the channel region. The current decrease is subsequently formulated with a stretched exponential model with bias‐dependent parameters for quantitative circuit analysis under off‐state degradation. A neural network hardware acceleration simulator is utilized to assess the complicated impact the off‐state current degradation could instigate on on‐chip trainable IGZO TFT‐based synapse arrays. The simulation results generally demonstrate deteriorated training accuracy with aggravated off‐state instability, and the accuracy trend is elucidated from the perspective of weight symmetry point.

Abstract Background Although the mpox global health emergency caused by mpox virus (MPXV) clade IIb.1 has ended, mpox cases are still reported due to low vaccination coverage and waning immunity. COH04S1 is a clinically evaluated, multiantigen COVID-19 vaccine candidate built on a fully synthetic platform of the highly attenuated modified vaccinia Ankara (MVA) vector, representing the only FDA-approved smallpox/mpox vaccine JYNNEOS. Given the potential threat of MPXV resurgence and need for vaccine alternatives, we aimed to assess the capacity COH04S1 and its synthetic MVA (sMVA) backbone to confer MPXV-specific immunity. Methods We evaluated orthopoxvirus-specific and MPXV cross-reactive immune responses in samples collected during a Phase 1 clinical trial of COH04S1 and in non-human primates (NHP) vaccinated with COH04S1 or its sMVA backbone. MPXV cross-reactive immune responses in COH04S1-vaccinated healthy adults were compared to responses measured in healthy subjects vaccinated with JYNNEOS. Additionally, we evaluated the protective efficacy of COH04S1 and sMVA against mpox in mpox-susceptible CAST/EiJ mice. Results COH04S1-vaccinated individuals develop robust orthopoxvirus-specific humoral and cellular responses, including cross-reactive antibodies to MPXV-specific virion proteins as well as MPXV cross-neutralizing antibodies in 45% of the subjects. In addition, NHP vaccinated with COH04S1 or sMVA show similar MPXV cross-reactive antibody responses. Moreover, MPXV cross-reactive humoral responses elicited by COH04S1 are comparable to those measured in JYNNEOS-vaccinated subjects. Finally, we show that mice vaccinated with COH04S1 or sMVA are protected from lung infection following challenge with MPXV clade IIb.1. Conclusions These results demonstrate the capacity of sMVA vaccines to elicit cross-reactive and protective orthopox-specific immunity against MPXV, suggesting that COH04S1 and sMVA could be developed as bivalent or monovalent mpox vaccine alternatives against MPXV.

The emergence of anti-influenza drug-resistant strains poses a challenge for influenza therapy due to mutations in the virus’s surface protein. Recently, there has been increasing interest in combination therapy consisting of two or more drugs as a potential alternative approach, aiming to enhance therapeutic efficacy. In this study, we investigated a novel synergistic therapy with a vertical effect using a single-domain VL-HA1-specific antibody against H1N1/PR8 and a horizontal effect using an RNA catalytic antibody with broad-spectrum influenza antiviral drug. We isolated a single-domain VL-HA1-specific (NVLH8) antibody binding to the virus particles showing a neutralizing activity against influenza virus A, specifically H1N1/PR8, as determined by the reduction in plaque number and lower viral HA protein expression in vitro. The neutralizing antibody likely prevented the viral entry, specifically at the viral genome-releasing step. Additionally, the 3D8 scFv hydrolyzed viral RNAs in the cytoplasm, including mRNA, vRNA, and cRNA in MDCK cells. The combined treatment of neutralizing antibodies for a vertical effect and 3D8 scFv for a horizontal effect produced a synergistic effect providing a novel approach against viral diseases when compared with a single treatment. Our results indicated that combining treatment, in particular two proteins exhibiting different mechanisms of action increased the antiviral activity against the influenza virus.

Abstract The demand for advanced packaging is driven by the need for low-profile, densely-integrated, large-die Si devices in substrate-based or wafer-level packaging. Die strength is a critical parameter for ultrathin dies, making die singulation a vital aspect of advanced packaging technology. In this work, we present a dicing before grinding (DBG) process to compare and analyze die strengths using a mechanical blade, stealth laser, and plasma dicing. The three DBG processes were applied to a 200 mm silicon (Si) wafer process with a die size of 10 × 10 mm2 and thicknesses of 100, 200, and 300 μm, respectively. Optical and electron microscopes were employed to investigate chipping quality, sidewall damage, and surface contamination. The bare Si die’s strength was assessed using a three-point bending test. Plasma dicing before grinding (PDBG) resulted in less contamination, chipping, and cracking compared to other DBG processes. Furthermore, PDBG exhibited the highest die strength of 1052 Pa.

Abstract Immune checkpoint inhibitor (ICI) therapy has had limited success (

In the autonomous driving industry, there is a growing trend to employ long-wave infrared (LWIR)-based uncooled thermal-imaging cameras, capable of robustly collecting data even in extreme environments. Consequently, both industry and academia are actively researching contrast-enhancement techniques to improve the quality of LWIR-based thermal-imaging cameras. However, most research results only showcase experimental outcomes using mass-produced products that already incorporate contrast-enhancement techniques. Put differently, there is a lack of experimental data on contrast enhancement post-non-uniformity (NUC) and temperature compensation (TC) processes, which generate the images seen in the final products. To bridge this gap, we propose a histogram equalization (HE)-based contrast enhancement method that incorporates a region-based clipping technique. Furthermore, we present experimental results on the images obtained after applying NUC and TC processes. We simultaneously conducted visual and qualitative performance evaluations on images acquired after NUC and TC processes. In the visual evaluation, it was confirmed that the proposed method improves image clarity and contrast ratio compared to conventional HE-based methods, even in challenging driving scenarios such as tunnels. In the qualitative evaluation, the proposed method demonstrated upper-middle-class rankings in both image quality and processing speed metrics. Therefore, our proposed method proves to be effective for the essential contrast enhancement process in LWIR-based uncooled thermal-imaging cameras intended for autonomous driving platforms.

Abstract Palladium is the most prominent material in both scientific and industrial research on gas storage, purification, detection, and catalysis due to its unique properties as a catalyst and hydrogen absorber. Advancing the dynamic optical phenomena of palladium reacting with hydrogen, transduction of the gas-matter reaction into light-matter interaction is attempted to visualize the dynamic surface chemistry and reaction behaviors. The simple geometry of the metal-dielectric-metal structure, Fabry–Perot etalon, is employed for a colorimetric reactor, to display the catalytic reaction of the exposed gas via water-film/bubble formation at the dielectric/palladium interface. The adsorption/desorption behavior and catalytic reaction of hydrogen and oxygen on the palladium surface display highly repeatable and dramatic color changes based on two distinct water formation trends: the foggy effect by water bubbles and the whiteout effect by water film formation. Simulations and experiments demonstrate the robustness of the proposed Fabry–Perot etalon as an excellent platform for monitoring the opto-physical phenomena driven by heterogeneous catalysis.

In comparison to other countries, South Korea has a high reliance on energy, with the majority of its electricity being generated by a government-run company to ensure a stable and affordable supply. Unlike the ''pay-as-bid'' pricing approach, South Korea utilizes the system marginal price (SMP), also known as ''pay-as-clear.'' Accurate SMP forecasting is crucial for guaranteeing steady economic growth because manufacturing is South Korea's flagship industry and accounts for 50 % of the nation's power consumption. In this study, a combination of machine learning-based batch learning and online learning techniques were employed to forecast the SMP in South Korea, utilizing a dataset consisting of five energy sectors, two financial sectors, and one transportation sector. The analysis of the F-value revealed that the coal sector, which is one of the energy sectors, had the most significant influence on SMP indicating the greatest score of 2,328. In this study, three machine learning models, namely support vector regression, simple deep neural network, and deep neural network, were suggested and compared for batch learning to determine the best-trained model. The evaluation metrics were used to assess the performance of these models. Based on the results obtained, the simple deep neural network was found to outperform the other models in terms of accuracy. Furthermore, two methods such as weight modification and time interval updating between inputs and output were employed for online learning based on the trained batch model. Upon the implementation of model updates, an ongoing assessment of its performance transpired utilizing the metrics of coefficient of determination, root mean square error, mean absolute error, and mean absolute percentage error. The average values for these metrics were observed to be 0.924, 7.991, 5.035, and 0.052, respectively. This study is expected to provide direct assistance in the formulation of energy plans for decision-makers in the industrial sector.

Abstract Biomolecular condensates play a key role in organizing cellular reactions by concentrating a specific set of biomolecules. However, whether condensate formation is accompanied by an increase in the total mass concentration within condensates or by the demixing of already highly crowded intracellular components remains elusive. Here, using refractive index imaging, we quantify the mass density of several condensates, including nucleoli, heterochromatin, nuclear speckles, and stress granules. Surprisingly, the latter two condensates exhibit low densities with a total mass concentration similar to the surrounding cyto- or nucleoplasm. Low-density condensates display higher permeability to cellular protein probes. We find that RNA tunes the biomolecular density of condensates. Moreover, intracellular structures such as mitochondria heavily influence the way phase separation proceeds, impacting the localization, morphology, and growth of condensates. These findings favor a model where segregative phase separation driven by non-associative or repulsive molecular interactions together with RNA-mediated selective association of specific components can give rise to low-density condensates in the crowded cellular environment.

Abstract Emerging SARS-CoV-2 Omicron subvariants continue to disrupt COVID-19 vaccine efficacy through multiple immune mechanisms including neutralizing antibody evasion. We developed COH04S1, a synthetic modified vaccinia Ankara vector that co-expresses Wuhan-Hu-1-based spike and nucleocapsid antigens. COH04S1 demonstrated efficacy against ancestral virus and Beta and Delta variants in animal models and was safe and immunogenic in a Phase 1 clinical trial. Here, we report efficacy of COH04S1 and analogous Omicron BA.1- and Beta-specific vaccines to protect Syrian hamsters from Omicron subvariants. Despite eliciting strain-specific antibody responses, all three vaccines protect hamsters from weight loss, lower respiratory tract infection, and lung pathology following challenge with Omicron BA.1 or BA.2.12.1. While the BA.1-specifc vaccine affords consistently improved efficacy compared to COH04S1 to protect against homologous challenge with BA.1, all three vaccines confer similar protection against heterologous challenge with BA.2.12.1. These results demonstrate efficacy of COH04S1 and variant-specific derivatives to confer cross-protective immunity against SARS-CoV-2 Omicron subvariants.

Abstract Purpose The aim of this study was to compare the anatomical structures of the acetabular rim around the anterior inferior iliac spine (AIIS) ridge that indicate anterior focal coverage of acetabulum between the sexes using a three-dimensional (3D) model. Methods 3D models of 71 adults (38 men and 33 women) with normal hip joints were used. Based on the location of the inflection point (IP) of the acetabular rim around the AIIS ridge, the patients were classified into anterior and posterior types, and the ratios thereof for each sex were compared. Coordinates for the IP, the most anterior point (MAP), and the most lateral point (MLP) were obtained and compared between the sexes and between anterior and posterior types. Results Coordinates for IPs in men were located anterior and inferior to those in women. MAP coordinates for men were located inferior to those for women, and MLP coordinates for men were located lateral and inferior to those for women. Comparing AIIS ridge types, we noted that coordinates for IPs of the anterior type were located medial, anterior, and inferior to those of the posterior type. Meanwhile, MAP coordinates of the anterior type were located inferior to those of the posterior type, and MLP coordinates of the anterior type were located lateral and inferior to those of the posterior type. Conclusion Anterior focal coverage of the acetabulum appears to differ between the sexes, and this difference may affect the development of pincer-type femoroacetabular impingement (FAI). Additionally, we found that anterior focal coverage differs according to anterior or posterior positioning of the bony prominence around the AIIS ridge, which may affect development of FAI.

Bentonite buffer material is a critical component in an engineered barrier system (EBS) for disposing high-level radioactive waste (HLW). The bentonite buffer material protects the disposal canister from groundwater penetration and releases decay heat to the surrounding rock mass; thus, it should possess high thermal conductivity, low hydraulic conductivity, and moderate swelling pressure to safely dispose the HLWs. Bentonite clay is a suitable buffer material because it satisfies the safety criteria. Several additives have been suggested as mixtures with bentonite to increase the thermal-hydraulic-mechanical-chemical (THMC) properties of bentonite buffer materials. Therefore, this study investigated the geotechnical, mineralogical, and THMC properties of several candidate additives such as sand, graphite, granite, and SiC powders. Datasets obtained in this study can be used to select adequate additives to improve the THMC properties of the buffer material.

This study investigated the locomotion mechanism of foxtail robots, focusing on the frictional anisotropy of tilted bristles under the same friction coefficient and propulsion strategy using bristle diversity. Through dynamic analysis and simulations, we confirmed the frictional anisotropy of tilted bristles and elucidated the role of bristle diversity in generating propulsive force. The interaction between contact nonuniformity and frictional anisotropy was identified as the core principle enabling foxtail locomotion. Simulations of foxtail robots with multiple bristles demonstrated that variations in bristle length, angle, and deformation contribute to propulsive force generation and environmental adaptability. In addition, this study analyzed the influence of major design parameters on frictional anisotropy, highlighting the critical roles of body height, bristle length, stiffness, reference angle, and friction coefficient. The proposed guidelines for designing foxtail robots emphasize securing bristle nonuniformity and inducing contact nonuniformity. The simulation framework presented enables the quantitative prediction and optimization of foxtail robot performance. This research provides valuable insights into foxtail robot locomotion and lays a foundation for the development of efficient and adaptive next-generation robots for diverse environments.

In the field of robotics and autonomous driving, dynamic occupancy grid maps (DOGMs) are typically used to represent the position and velocity information of objects. Although three-dimensional light detection and ranging (LiDAR) sensor-based DOGMs have been actively researched, they have limitations, as they cannot classify types of objects. Therefore, in this study, a deep learning-based camera–LiDAR sensor fusion technique is employed as input to DOGMs. Consequently, not only the position and velocity information of objects but also their class information can be updated, expanding the application areas of DOGMs. Moreover, unclassified LiDAR point measurements contribute to the formation of a map of the surrounding environment, improving the reliability of perception by registering objects that were not classified by deep learning. To achieve this, we developed update rules on the basis of the Dempster–Shafer evidence theory, incorporating class information and the uncertainty of objects occupying grid cells. Furthermore, we analyzed the accuracy of the velocity estimation using two update models. One assigns the occupancy probability only to the edges of the oriented bounding box, whereas the other assigns the occupancy probability to the entire area of the box. The performance of the developed perception technique is evaluated using the public nuScenes dataset. The developed DOGM with object class information will help autonomous vehicles to navigate in complex urban driving environments by providing them with rich information, such as the class and velocity of nearby obstacles.

Recent advancements in deep learning have brought significant improvements to plant disease recognition. However, achieving satisfactory performance often requires high-quality training datasets, which are challenging and expensive to collect. Consequently, the practical application of current deep learning–based methods in real-world scenarios is hindered by the scarcity of high-quality datasets. In this paper, we argue that embracing poor datasets is viable and aims to explicitly define the challenges associated with using these datasets. To delve into this topic, we analyze the characteristics of high-quality datasets, namely, large-scale images and desired annotation, and contrast them with the limited and imperfect nature of poor datasets. Challenges arise when the training datasets deviate from these characteristics. To provide a comprehensive understanding, we propose a novel and informative taxonomy that categorizes these challenges. Furthermore, we offer a brief overview of existing studies and approaches that address these challenges. We point out that our paper sheds light on the importance of embracing poor datasets, enhances the understanding of the associated challenges, and contributes to the ambitious objective of deploying deep learning in real-world applications. To facilitate the progress, we finally describe several outstanding questions and point out potential future directions. Although our primary focus is on plant disease recognition, we emphasize that the principles of embracing and analyzing poor datasets are applicable to a wider range of domains, including agriculture. Our project is public available at https://github.com/xml94/EmbracingLimitedImperfectTrainingDatasets.

The presence of macroroughness elements directly affects the flow velocity in mountain headwater streams. Hydraulic roughness is the dominant resistance to flow caused by objects protruding into the water, but it is not measurable in the field. This study quantified the reach-average hydraulic roughness based on the channel morphology in two mountain streams. The average flow velocities of the reaches were measured using the dye-tracing method. The magnitude of the hydraulic roughness was derived from the grain size of the streambed materials (D50 and D84) and the cross-sectional/longitudinal bed roughness. The observations for low flows (0.04–0.43 m2/s discharge per unit width) indicated that the longitudinal 90% inter-percentile range (IPR90L) seemed to have considerable merit in examining the influences of large roughness elements on flow conveyance. A dimensionless hydraulic geometry relation that can reflect the field measurements over a limited range of hydraulic characteristics was also developed for estimating the reach-average flow velocity in steep and rough streams. Thus, the research framework used appears to provide a reliable method of quantifying reach-average hydraulic roughness from local data in mountain headwater streams.

Bars drive gas inflow. As the gas flows inward, shocks and shear occur along the bar dust lanes. Such shocks and shear can affect the star formation (SF) and change the gas properties. For four barred galaxies, we present H α velocity gradient maps that highlight bar-driven shocks and shear using data from the PHANGS-MUSE and PHANGS-ALMA surveys, which allow us to study bar kinematics in unprecedented detail. Velocity gradients are enhanced along the bar dust lanes, where shocks and shear are shown to occur in numerical simulations. Velocity gradient maps also efficiently pick up H ii regions that are expanding or moving relative to the surroundings. We put pseudo-slits on the regions where velocity gradients are enhanced and find that H α and CO velocities jump up to ∼170 km s ^−1 , even after removing the effects of circular motions due to the galaxy rotation. Enhanced velocity gradients either coincide with the peak of CO intensity along the bar dust lanes or are slightly offset from CO intensity peaks, depending on the objects. Using the Baldwin–Philips–Terlevich BPT diagnostic, we identify the source of ionization on each spaxel and find that SF is inhibited in the high-velocity gradient regions of the bar, and the majority of those regions are classified as a low-ionization nuclear emission-line region (LINER) or composite. This implies that SF is inhibited where bar-driven shear and shocks are strong. Our results are consistent with the results from the numerical simulations that show SF is inhibited in the bar where the shear force is strong.

Variational quantum machine learning (VQML) models based on parameterized quantum circuits (PQC) have been expected to offer a potential quantum advantage for machine learning (ML) applications. However, comparison between VQML models and their classical counterparts is hard due to the lack of interpretability of VQML models. In this study, we introduce a graphical approach to analyze the PQC and the corresponding operation of VQML models to deal with this problem. In particular, we utilize the Stokes representation of quantum states to treat VQML models as network models based on the corresponding representations of basic gates. From this approach, we suggest the notion of active paths in the networks and relate the expressivity of VQML models with it. We investigate the growth of active paths in VQML models and observe that the expressivity of VQML models can be significantly limited for certain cases. Then we construct classical models inspired by our graphical interpretation of VQML models and show that they can emulate or outperform the outputs of VQML models for these cases. Our result provides a new way to interpret the operation of VQML models and facilitates the interconnection between quantum and classical ML areas.

In the original publication [...]

In multi-cat households, monitoring individual cats’ various behaviors is essential for diagnosing their health and ensuring their well-being. This study focuses on the defecation and urination activities of cats, and introduces an adaptive cat identification architecture based on deep learning (DL) and machine learning (ML) methods. The architecture comprises an object detector and a classification module, with the primary focus on the design of the classification component. The DL object detection algorithm, YOLOv4, is used for the cat object detector, with the convolutional neural network, EfficientNetV2, serving as the backbone for our feature extractor in identity classification with several ML classifiers. Additionally, to address changes in cat composition and individual cat appearances in multi-cat households, we propose an adaptive concept drift approach involving retraining the classification module. To support our research, we compile a comprehensive cat body dataset comprising 8934 images of 36 cats. After a rigorous evaluation of different combinations of DL models and classifiers, we find that the support vector machine (SVM) classifier yields the best performance, achieving an impressive identification accuracy of 94.53%. This outstanding result underscores the effectiveness of the system in accurately identifying cats.

Fires can burn canopy fuel and replace leafy crowns with charred branches and trunks, thereby affecting hydrological flow and water chemistry. However, little is known about the effects of fire on throughfall volumes and chemical fluxes in temperate forests. Therefore, we aimed to monitor the volume and chemistry of throughfall in pine trees (Pinus densiflora) damaged by the 2022 Gangneung-Donghae Forest fire in the Republic of Korea. Immediately after the forest fire, funnel-type measurements were performed to collect the throughfall beneath five trees at foliage necrosis and crown consumption sites. The amount of water that penetrated in a specified period was continually measured and analyzed in terms of the water quality components. Crown consumption resulted in the passage of more water due to the removal of the tree canopy; however, the ratio of throughfall to total rainfall remained constant as the rainfall amount increased. The throughfall volume was not significantly different owing to the fire damage. The solute concentrations of Ca and TOC at the crown consumption site were higher than those at the foliage necrosis site after the fire; however, no significant difference was observed three months after the fire. In this study, the changes in the amount and water quality of throughfall due to fire were examined over a relatively short period, providing fundamental data for nutrient cycling management of wildfire-damaged soil.

Abstract Backgrounds The basic method of surgical treatment for extracapsular hip fractures (ECFs), including intertrochanteric fracture and basicervical fracture (BCF), is osteosynthesis. Intramedullary nails are among the most commonly used fixation devices for these fractures. Our study aimed to report the clinical outcomes of ECF treatment with two different nail devices and to analyze the risk factors associated with screw cut-out. Methods We retrospectively reviewed the medical records of 273 patients (300 cases) from a single institution who underwent surgical treatment for ECF between January 2013 and October 2018. Overall, 138 patients were eligible for the study and were divided into two groups according to the osteosynthesis device used. We evaluated the clinical outcomes of fracture surgery and performed univariate and multivariate regression analyses to identify risk factors associated with screw cut-out in each group. Results We used proximal femoral nails (group 1) to treat 83 patients and cephalomedullary nails (group 2) to treat 55 patients. Nine cut-outs (group 1, 6 cases; group 2, 3 cases) occurred during follow-up. The patients’ high body mass index (BMI) (p = 0.019), BCFs (p = 0.007), non-extramedullary reduction in the anteroposterior and lateral planes (p = 0.032 and p = 0.043, respectively), and anti-rotation screw pull-outs (p = 0.041) showed a positive correlation to screw cut-out in the univariate analysis of group 1. In group 2, only BCFs was positively correlated (p = 0.020). In the multivariate analysis of group 1, the patients’ BMIs (p = 0.024) and BCFs (p = 0.024) showed a positive correlation with cut-out. Meanwhile, the multivariate analysis of group 2 did not identify any factors associated with cut-out. Conclusions The cut-out risk was significantly higher in the BCF cases, regardless of the nail design used. Considerable attention should be paid to treating such unstable fractures. We expect that new-generation nails using a helical blade, or interlocking derotation and interlocking screws may improve surgical outcomes.

Abstract The outbreak of novel COVID-19 disease elicited a wide range of anti-contagion and economic policies like school closure, income support, contact tracing, and so forth, in the mitigation and suppression of the spread of the SARS-CoV-2 virus. However, a systematic evaluation of these policies has not been made. Here, 17 implemented policies from the Oxford COVID-19 Government Response Tracker dataset employed in 90 countries from December 31, 2019, to August 31, 2020, were analyzed. A Poisson regression model was applied to analyze the relationship between policies and daily confirmed cases using a generalized estimating equations approach. A lag is a fixed time displacement in time series data. With that, lagging (0, 3, 7, 10, and 14 days) was also considered during the analysis since the effects of policies implemented on a given day may affect the number of confirmed cases several days after implementation. The countries were divided into three groups depending on the number of waves of the pandemic observed in each country. Through subgroup analysis, we showed that with and without lagging, contact tracing and containment policies were significant for countries with two waves, while closing, economic, and health policies were significant for countries with three waves. Wave-specific analysis for each wave showed that significant health, economic, and containment policies varied across waves of the pandemic. Emergency investment in healthcare was consistently significant among the three groups of countries, while the Stringency index was significant among all waves of the pandemic. These findings may help in making informed decisions regarding whether, which, or when these policies should be intensified or lifted.

Abstract Quantifying the spin–orbit torque (SOT) efficiency with changing the layer thickness is crucial for understanding the physical background of SOT. This study investigates the Nb-thickness-dependent SOT efficiency of two types of layered heterostructures: Ta/Nb/CoFeB and Pt/Nb/CoFeB. We find that the Nb thickness dependence of the SOT efficiency in the two samples is quite different. In the Pt/Nb series, the SOT sign changes according to the thickness variation because Pt and Nb have different spin–orbit coupling signs. We observe the resulting reversal in switching polarity through current-induced SOT switching experiments. However, due to the same spin–orbit coupling signs of Ta and Nb, no such polarity reversal was observed in Ta/Nb series. Further, we extract the spin diffusion length of Nb in each heterostructure. These results provide a systematic understanding of the material- and thickness-dependent SOT characteristics.

For confidence of memory, a neural basis such as traces of stored memories should be required. However, because false memories have never been stored, the neural basis for false memory confidence remains unclear. Here we monitored the brain activity in participants while they viewed learned or novel objects, subsequently decided whether each presented object was learned and assessed their confidence levels. We found that when novel objects are presented, false memory confidence significantly depends on the shared representations with learned objects in the prefrontal cortex. However, such a tendency was not found in posterior regions including the visual cortex, which may be involved in the processing of perceptual gist. Furthermore, the confidence-dependent shared representations were not observed when participants correctly answered novel objects as non-learned objects. These results demonstrate that false memory confidence is critically based on the reinstatement of high-level semantic gist of stored memories in the prefrontal cortex.

The study of water at high pressure and temperature is essential for understanding planetary interiors but is hampered by the high reactivity of water at extreme conditions. Here, indirect X-ray laser heating of water in a diamond anvil cell is realized via a gold absorber, showing no evidence of reactivity.

The mechanism of the interfacial Dzyaloshinskii-Moriya interaction in heavy metal-ferromagnet heterostructures is debated. Here, the authors show the oscillating behaviour of the interaction as a function of the MgO spacer layer thickness, supporting the interlayer exchange coupling mechanism of the Ruderman-Kittel-Kasuya-Yosida type.

Sleep deprivation is known to have adverse effects on various cognitive abilities. In particular, a lack of sleep has been reported to disrupt memory consolidation and cognitive control functions. Here, focusing on long-term memory and cognitive control processes, we review the consistency and reliability of the results of previous studies of sleep deprivation effects on behavioral performance with variations in the types of stimuli and tasks. Moreover, we examine neural response changes related to these behavioral changes induced by sleep deprivation based on human fMRI studies to determine the brain regions in which neural responses increase or decrease as a consequence of sleep deprivation. Additionally, we discuss about the possibility that light as an environmentally influential factor affects our sleep cycles and related cognitive processes.

In this study, the permeabilities of Berea and Otway sandstones were measured under different confining pressures, and porosity was investigated through mercury intrusion porosimetry (MIP). The total porosities of the Berea and Otway sandstones were approximately 17.4% and 25%, respectively. Pore size distributions of each sandstone were almost the same, but the pores in the Otway sandstone were slightly narrower. However, the permeability of the Otway sandstone was smaller than that of the Berea sandstone by one order of magnitude. Three-dimensional (3D) void geometry and geometrical properties of the void spaces relevant to flow were compared to obtain the relation between the permeability differences and porosities of the two sandstones. The 3D geometrical analysis using microfocus X-ray computed tomography (CT) was performed, and the pore geometries of both sandstones were compared using the 3D medial axis (3DMA) method. Pore and throat radii, pore coordination number, tortuosity, number of connecting paths, connecting path volume, and other factors were determined using 3DMA. The Otway sandstone was characterized by a small effective throat/pore radius ratio. Based on the fluid flow mechanism, the lower effective throat/pore radius ratio results in a lower permeability induced by the fluid energy loss, which means that the 3D geometrical shape of void spaces affects the permeability value.

Along the cold subduction geotherm, glaucophane remains stable down to pressure and temperature (P–T) conditions of ca. 240 km depth, whereas under the warm subduction geotherm, it dehydrates and breaks down into pyroxenes and silica between ca. 50 and 100 km depths.

The efficiency of photosynthesis in strawberry plants is measured to maintain the quality and quantity of strawberries produced. The latest method used to measure the photosynthetic status of plants is chlorophyll fluorescence imaging (CFI), which has the advantage of obtaining plant spatiotemporal data non-destructively. This study developed a CFI system to measure the maximum quantum efficiency of photochemistry (Fv/Fm). The main components of this system include a chamber for plants to adapt to dark environments, blue LED light sources to excite the chlorophyll in plants, and a monochrome camera with a lens filter attached to capture the emission spectra. In this study, 120 pots of strawberry plants were cultivated for 15 days and divided into four treatment groups: control, drought stress, heat stress, and a combination of drought and heat stress, resulting in Fv/Fm values of 0.802 ± 0.0036, 0.780 ± 0.0026, 0.768 ± 0.0023, and 0.749 ± 0.0099, respectively. A strong correlation was found between the developed system and a chlorophyll meter (r = 0.75). These results prove that the developed CFI system can accurately capture the spatial and temporal dynamics resulting from the response of strawberry plants to abiotic stresses.

Background/Aims Oral sulfate solution (OSS) is an emerging cleansing agent for bowel preparation. However, data comparing OSS to other conventional bowel preparations in Asian patients are limited. Therefore, the objective of this study was to compare the efficacy and tolerability of OSS to ascorbic acid plus polyethylene glycol (AA + PEG) in Asian patients. Methods This was a prospective, randomized, parallel, investigator-blind study performed in two university hospitals in Korea. Bowel preparation efficacy was evaluated using both the Ottawa Bowel Preparation Scale (OBPS) and Boston Bowel Preparation Scale (BBPS). Results Among 173 patients, 86 received OSS while 87 received AA + PEG for bowel preparation. Total OBPS score was 2.80 ± 2.48 in the OSS group and 4.49 ± 3.08 in the AA + PEG group, indicating significantly (p < 0.001) better efficacy with OSS. Total BBPS was higher in the OSS group (7.43 ± 1.49 vs. 6.51 ± 1.76, p < 0.001), indicating superior bowel preparation quality with OSS. Preparation-related adverse events were generally acceptable. Patients receiving OSS had more nausea (1.92 ± 0.94 vs. 1.54 ± 0.76, p = 0.004) and abdominal cramping (1.45 ± 0.78 vs. 1.17 ± 0.51, p = 0.006) than those receiving AA + PEG. However, overall satisfaction and taste were similar between the two groups. Conclusions OSS had a non-inferior bowel cleansing efficacy than AA + PEG regardless of colon segment.

Label-free trapping of nanoparticles via dielectophoretic forces is traditionally done with electrodes in a horizontal gap layout. Here, the authors present a vertical nanogap architecture, which allows for precise capture and spatiotemporal manipulation of nanoparticles and molecular assemblies.

NTRUEncrypt is a public key cryptosystem based on hard problems over lattices. The dominant operation in NTRUEncrypt is convolution, i.e., multiplication over a quotient ring of polynomials. Based on the fact that a convolution has a highly regular structure, Lee et al. proposed the sliding window method for fast convolution of binary polynomials in 2013, which was then extended to ternary polynomials for ideal lattices by Akleylek, Alkim, and Tok in 2016. These sliding window methods reduce the cost of a convolution operation using look-up tables that store partial computation results related to repeated coefficient patterns. In this paper, we propose a signed sliding window method with side-channel resistance for NTRUEncrypt. The proposed method considers both positive and negative nonzero coefficients when constructing look-up tables. The new method not only accelerates convolution but also enables the application of power analysis countermeasures effectively. According to the experimental results, the constant-time implementation of the proposed method with timing and power analysis countermeasures accelerates the previously developed secure convolution method by up to 20%.

Adeno-associated viruses (AAVs) have been well characterized and used to deliver therapeutic genes for diseases treatment in clinics and basic research. This study used the triple transient transfection of AAV-DJ/8 as a model expression system to develop and optimize the laboratory production of AAV for research and pre-clinical applications. Specifically, various production parameters, including host cell, transfection reagent, cell density, ratio of plasmid DNA and cells, gene size, and production mode, were tested to determine the optimal process. Our results showed that the adherent production using HEK 293AAV with calcium transfection generated the highest volumetric productivity of 7.86 × 109 gc/ml. The optimal suspensive production using HEK 293F had best AAV productivity of 5.78 × 109 gc/ml in serum-free medium under transfection conditions of transfection density of 0.4 × 106 cells/ml, plasmid DNA:cells ratio of 1.6 µg:106 cells and synthesized cationic liposomes as transfection reagent. The similar AAV productivity was confirmed at scales of 30–450 ml in shaker and/or spinner flasks. The in vitro transfection and in vivo infection efficiency of the harvested AAV-DJ/8 carrying luciferase reporter gene was confirmed using cell line and xenograft mouse model, respectively. The minimal or low purification recovery rate of AAV-DJ/8 in ion-exchange chromatography column and affinity column was observed in this study. In summary, we developed and optimized a scalable suspensive production of AAV to support the large-scale preclinical animal studies in research laboratories.

BackgroundThe influence of community context and individual socioeconomic status on health is widely recognized. However, the dynamics of how the relationship of neighborhood context on health varies by individual socioeconomic status is less well understood.ObjectiveTo examine the relationship between neighborhood context and mortality among older adults and examine how the influence of neighborhood context on mortality differs by individual socioeconomic status, using two measures of income-level and homeownership.Research design and subjectsA retrospective study of 362,609 Medicare Advantage respondents to the 2014-2015 Medicare Health Outcomes Survey aged 65 and older.MeasuresNeighborhood context was defined using the deciles of the Area Deprivation Index. Logistic regression was used to analyze mortality with interaction terms between income/homeownership and neighborhood deciles to examine cross-level relationships, controlling for age, gender, race/ethnicity, number of chronic conditions, obese/underweight, difficulties in activities of daily living, smoking status, and survey year. Predicted mortality rates by group were calculated from the logistic model results.ResultsLow-income individuals (8.9%) and nonhomeowners (9.1%) had higher mortality rates compared to higher-income individuals (5.3%) and homeowners (5.3%), respectively, and the differences were significant across all neighborhoods even after adjustment. With regression adjustment, older adults residing in less disadvantaged neighborhoods showed lower predicted 2-year mortality among high-income (4.86% in the least disadvantaged neighborhood; 6.06% in the most disadvantaged neighborhood; difference p-valueConclusionsLow-income or non-homeowning older adults had a higher risk of mortality regardless of neighborhood socioeconomic status. While living in a less disadvantaged neighborhood provided a protective association for higher-income or homeowning older adults, low-income older adults did not experience an observable benefit.

Flu disease, with high mortality and morbidity, is caused by the influenza virus. Influenza infections are most effectively prevented through vaccination, but it requires annual reformulation due to the antigenic shift or drift of hemagglutinin and neuraminidase proteins. Increasing resistance to available anti-influenza drugs was also recently reported. The M2 surface protein of the influenza virus is an attractive target for universal vaccine development as it is highly conserved and multifunctional throughout the viral life cycle. This study aimed to discover a single-chain variable fragment (scFv) targeting the M2 protein of influenza A H1N1/PR8, showing neutralizing activity through plaque inhibition in virus replication. Several candidates were isolated using bio-panning, including scFv and single-domain VL target M2 protein, which was displayed on the yeast surface. The scFv/VL proteins were obtained with high yield and high purity through soluble expression in E. coli BL21 (DE3) pLysE strains. A single-domain VL-M2-specific antibody, NVLM10, exhibited the highest binding affinity to influenza virions and was engineered into a bivalent format (NVL2M10) to improve antigen binding. Both antibodies inhibited virus replication in a dose-dependent manner, determined using plaque reduction- and immunocytochemistry assays. Furthermore, bivalent anti-M2 single-domain VL antibodies significantly reduced the plaque number and viral HA protein intensity as well as viral genome (HA and NP) compared to the monovalent single-domain VL antibodies. This suggests that mono- or bivalent single-domain VL antibodies can exhibit neutralizing activity against influenza virus A, as determined through binding to virus particle activity.

The water retention capacity of forest leaf litter was estimated through lysimeter measurements under field conditions. Six lysimeters were placed in Pinus koraiensis and Quercus acutissima forests and filled with the surrounding leaf litter to represent the effects of litter type on the water retention capacity. Two years of measurements for rainfall and litter weight have been conducted in all lysimeters at 30 min intervals. Field measurements showed that P. koraiensis litter stored more water during rainfall periods than did Q. acutissima litter. As a result, immediately after the cessation of rainfall, 1.82 mm and 3.00 mm of water were retained per unit mass of Q. acutissima and P. koraiensis litter, respectively. Following rainfall, after the gravitational flow had entirely drained, the remaining water adhered to the litter was estimated to be 1.66 ± 1.72 mm and 2.72 ± 2.82 mm per unit mass per rainfall event for Q. acutissima and P. koraiensis litter, respectively. During the study period, approximately 83.7% of incident rainfall drained into the uppermost soil layer below the Q. acutissima litter, whereas 84.5% of rainfall percolated through the P. koraiensis litter. The moisture depletion curves indicated that 50% of the water retained in the Q. acutissima and P. koraiensis litter was lost via evaporation within 27 h and 90 h after the cessation of rainfall, respectively. This study demonstrated the water retention storage of leaf litter and its contribution to the water balance over floor litter according to litter and rainfall characteristics. The results also proved that lysimetry is a reliable method to quantify the variation of litter moisture under natural conditions.

Most microsensors are composed of devices and covers. Due to the complicated structure of the cover and various other requirements, it difficult to use wafer-level packaging with such microsensors. In particular, for monolithic microsensors combined with read-out ICs, the available process margins are further reduced due to the thermal and mechanical effects applied to IC wafers during the packaging process. This research proposes a low-temperature, wafer-level vacuum packaging technology based on Cu-Sn bonding and nano-multilayer getter materials for use with microbolometers. In Cu-Sn bonding, the Cu/Cu3Sn/Cu microstructure required to ensure reliability can be obtained by optimizing the bonding temperature, pressure, and time. The Zr-Ti-Ru based nanomultilayer getter coating inside the cap wafer with high step height has been improved by self-aligned shadow masking. The device pad, composed of bonded wafer, was opened by wafer grinding, and the thermoelectrical properties were evaluated at the wafer-level. The bonding strength and vacuum level were characterized by a shear test and thermoelectrical test using microbolometer test pixels. The vacuum level of the packaged samples showed very narrow distribution near 50 mTorr. This wafer-level packaging platform could be very useful for sensor development whereby high reliability and excellent mechanical/optical performance are both required. Due to its reliability and the low material cost and bonding temperature, this wafer-based packaging approach is suitable for commercial applications.

Recently, several plant pathogens have become more active due to temperature increases arising from climate change, which has caused damage to various crops. If climate change continues, it will likely be very difficult to maintain current crop production, and the problem of a shortage of expert manpower is also deepening. Fortunately, research on various early diagnosis systems based on deep learning is actively underway to solve these problems, but the problem of lack of diversity in some hard-to-collect disease samples remains. This imbalanced data increases the bias of machine learning models, causing overfitting problems. In this paper, we propose a data augmentation method based on an image-to-image translation model to solve the bias problem by supplementing these insufficient diseased leaf images. The proposed augmentation method performs translation between healthy and diseased leaf images and utilizes attention mechanisms to create images that reflect more evident disease textures. Through these improvements, we generated a more plausible diseased leaf image compared to existing methods and conducted an experiment to verify whether this data augmentation method could further improve the performance of a classification model for early diagnosis of plants. In the experiment, the PlantVillage dataset was used, and the extended dataset was built using the generated images and original images, and the performance of the classification models was evaluated through the test set.

Recent advances in automatic recognition systems based on deep learning technology have shown the potential to provide environmental-friendly plant disease monitoring. These systems are able to reliably distinguish plant anomalies under varying environmental conditions as the basis for plant intervention using methods such as classification or detection. However, they often show a performance decay when applied under new field conditions and unseen data. Therefore, in this article, we propose an approach based on the concept of open-set domain adaptation to the task of plant disease recognition to allow existing systems to operate in new environments with unseen conditions and farms. Our system specifically copes diagnosis as an open set learning problem, and mainly operates in the target domain by exploiting a precise estimation of unknown data while maintaining the performance of the known classes. The main framework consists of two modules based on deep learning that perform bounding box detection and open set self and across domain adaptation. The detector is built based on our previous filter bank architecture for plant diseases recognition and enforces domain adaptation from the source to the target domain, by constraining data to be classified as one of the target classes or labeled as unknown otherwise. We perform an extensive evaluation on our tomato plant diseases dataset with three different domain farms, which indicates that our approach can efficiently cope with changes of new field environments during field-testing and observe consistent gains from explicit modeling of unseen data.

The heat transfer characteristics in 180-degree turn region of two-pass channel with three different aspect ratios were investigated as coolant being extracted through tip-holes. The naphthalene sublimation technique was used for the heat/mass transfer measurements from the internal ceiling surface of the 180-degree turn region. The channel aspect-ratios of 1:1, 2:1, and 5:1 while keeping the same hydraulic diameter were studied. The inlet Reynolds number was set at 10000 that matches an actual land-based gas turbine. The tip-extraction mass flow rate was set at 10% of the total inlet mass flow rate. The experimental results showed that the heat/mass transfer is improved with the coolant extraction from tip-holes for all test cases. The improvement of thermal performance on tip surface is ranged from 10.8% to 67.0% compared to the no coolant extraction cases. The improvement was highest for the case of 1:5 aspect ratio as the extraction helped to minimize the region of local recirculation. The study proved that the tip-hole extraction could be effectively designed to improve the cooling of 180-degree turn region.

Ginseng is a perennial herbaceous plant that has been widely consumed for medicinal and dietary purposes since ancient times. Ginseng plants require shade and cool temperatures for better growth; climate warming and rising heat waves have a negative impact on the plants’ productivity and yield quality. Since Republic of Korea’s temperature is increasing beyond normal expectations and is seriously threatening ginseng plants, an early-stage non-destructive diagnosis of stressed ginseng plants is essential before symptomatic manifestation to produce high-quality ginseng roots. This study demonstrated the potential of fluorescence hyperspectral imaging to achieve the early high-throughput detection and prediction of chlorophyll composition in four varieties of heat-stressed ginseng plants: Chunpoong, Jakyeong, Sunil, and Sunmyoung. Hyperspectral imaging data of 80 plants from these four varieties (temperature-sensitive and temperature-resistant) were acquired before and after exposing the plants to heat stress. Additionally, a SPAD-502 meter was used for the non-destructive measurement of the greenness level. In accordance, the mean spectral data of each leaf were extracted from the region of interest (ROI). Analysis of variance (ANOVA) was applied for the discrimination of heat-stressed plants, which was performed with 96% accuracy. Accordingly, the extracted spectral data were used to develop a partial least squares regression (PLSR) model combined with multiple preprocessing techniques for predicting greenness composition in ginseng plants that significantly correlates with chlorophyll concentration. The results obtained from PLSR analysis demonstrated higher determination coefficients of R2val = 0.90, and a root mean square error (RMSE) of 3.59%. Furthermore, five proposed bands (683 nm, 688 nm, 703 nm, 731 nm, and 745 nm) by stepwise regression (SR) were developed into a PLSR model, and the model coefficients were used to create a greenness-level concentration in images that showed differences between the control and heat-stressed plants for all varieties.

BackgroundDespite the fact that the adoption rate of electronic health records has increased dramatically among high-income nations, it is still difficult to properly disseminate personal health records. Token economy, through blockchain smart contracts, can better distribute personal health records by providing incentives to patients. However, there have been very few studies regarding the particular factors that should be considered when designing incentive mechanisms in blockchain. ObjectiveThe aim of this paper is to provide 2 new mathematical models of token economy in real-world scenarios on health care blockchain platforms. MethodsFirst, roles were set for the health care blockchain platform and its token flow. Second, 2 scenarios were introduced: collecting life-log data for an incentive program at a life insurance company to motivate customers to exercise more and recruiting participants for clinical trials of anticancer drugs. In our 2 scenarios, we assumed that there were 3 stakeholders: participants, data recipients (companies), and data providers (health care organizations). We also assumed that the incentives are initially paid out to participants by data recipients, who are focused on minimizing economic and time costs by adapting mechanism design. This concept can be seen as a part of game theory, since the willingness-to-pay of data recipients is important in maintaining the blockchain token economy. In both scenarios, the recruiting company can change the expected recruitment time and number of participants. Suppose a company considers the recruitment time to be more important than the number of participants and rewards. In that case, the company can increase the time weight and adjust cost. When the reward parameter is fixed, the corresponding expected recruitment time can be obtained. Among the reward and time pairs, the pair that minimizes the company’s cost was chosen. Finally, the optimized results were compared with the simulations and analyzed accordingly. ResultsTo minimize the company’s costs, reward–time pairs were first collected. It was observed that the expected recruitment time decreased as rewards grew, while the rewards decreased as time cost grew. Therefore, the cost was represented by a convex curve, which made it possible to obtain a minimum—an optimal point—for both scenarios. Through sensitivity analysis, we observed that, as the time weight increased, the optimized reward increased, while the optimized time decreased. Moreover, as the number of participants increased, the optimization reward and time also increased. ConclusionsIn this study, we were able to model the incentive mechanism of blockchain based on a mechanism design that recruits participants through a health care blockchain platform. This study presents a basic approach to incentive modeling in personal health records, demonstrating how health care organizations and funding companies can motivate one another to join the platform.

Strawberry (Fragaria × ananassa Duch) plants are vulnerable to climatic change. The strawberry plants suffer from heat and water stress eventually, and the effects are reflected in the development and yields. In this investigation, potential chlorophyll-fluorescence-based indices were selected to detect the early heat and water stress in strawberry plants. The hyperspectral images were used to capture the fluorescence reflectance in the range of 500 nm–900 nm. From the hyperspectral cube, the region of interest (leaves) was identified, followed by the extraction of eight chlorophyll-fluorescence indices from the region of interest (leaves). These eight chlorophyll-fluorescence indices were analyzed deeply to identify the best indicators for our objective. The indices were used to develop machine-learning models to assess the performance of the indicators by accuracy assessment. The overall procedure is proposed as a new workflow for determining strawberry plants’ early heat and water stress. The proposed workflow suggests that by including all eight indices, the random-forest classifier performs well, with an accuracy of 94%. With this combination of the potential indices, namely the red-edge vegetation stress index (RVSI), chlorophyll B (Chl-b), pigment-specific simple ratio for chlorophyll B (PSSRb), and the red-edge chlorophyll index (CIREDEDGE), the gradient-boosting classifier performs well, with an accuracy of 91%. The proposed workflow works well with a limited number of training samples which is an added advantage.