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This study examines the impact of low-income assistance and electrification programs on a disadvantaged community in Southern California. An urban building energy model is paired with an AC power flow and electric distribution system degradation model to evaluate how the cost of energy, carbon emissions, and pollutant emissions change after applying building weatherization, energy efficiency, and electrification measures to the community. Results show that traditional weatherization and energy efficiency measures (upgrading lighting and appliances, improving insulation to current building code standards) are the most cost-effective, reducing the cost of energy and carbon emissions by 10–20 % for the current community. Heat pump water heaters offer a 40 % average reduction in carbon emissions and almost 50 % decrease in criteria pollutant emissions, but at a cost increase of 17–22 %. Appliance electrification also reduces carbon emissions 5–10 % but increases cost by 7 % to 25 %. For reducing carbon, government programs that support building electrification are most cost-effective when they combine switching from natural gas to electricity with high efficiency system. Electrifying hot water and appliances effectively reduces emissions but must be paired with improved low-income assistance programs to prevent increased energy burden for low-income families. The urban building energy model and electrical distribution simulations used in this study can be replicated in other low-income communities.

Abstract Background Neutrophil extracellular trap (NET) has been implicated in the pathology of multiple sclerosis (MS) and experimental autoimmune encephalomyelitis (EAE). However, the specific contributions of NLRP3, a NET-associated molecule, to EAE pathogenesis and its regulatory role in NET formation remain unknown. Methods To investigate the detrimental effect of NETs supported by NLRP3 in MS pathogenesis, we induced EAE in WT and NLRP3 KO mice and monitored the disease severity. At the peak of the disease, NET formation was assessed by flow cytometry, immunoblotting, and immunofluorescence staining. To further identify the propensity of infiltrated neutrophils, NET-related chemokine receptors, degranulation, ROS production, and PAD4 expression levels were evaluated by flow cytometry. In some experiments, mice were injected with DNase-1 to eliminate the formed NETs. Results Our data revealed that neutrophils significantly infiltrate the brain and spinal cord and form NETs during EAE pathogenesis. NLRP3 significantly elevates NET formation, primarily in the brain. NLRP3 also modulated the phenotypes of brain-infiltrated and circulating neutrophils, augmenting CXCR2 and CXCR4 expression, thereby potentially enhancing NET formation. NLRP3 facilitates NET formation in a ROS-dependent and PAD4-independent manner in brain-infiltrated neutrophils. Finally, NLRP3-supported NET formation exacerbates disease severity, triggering Th1 and Th17 cells recruitment. Conclusions Collectively, our findings suggest that NLRP3-supported NETs may be an etiological factor in EAE pathogenesis, primarily in the brain. This study provides evidence that targeting NLRP3 could be a potential therapeutic strategy for MS, specifically by attenuating NET formation.

Abstract Perovskite‐oxide‐based capacitors, which exhibit high charge storage capacity, have attracted considerable attention as a potential candidate for overcoming the limitations of nanoscale integration. Unfortunately, a dead layer forms in these capacitors at the interface between the electrode and the dielectric, which degrades the charge storage capacity; thus, this layer has been extensively investigated. The dead layer in perovskite‐oxide‐based capacitors exhibits different characteristics depending on the electrode materials; however, a method for minimizing this layer is lacking. In this study, the charge storage capacity of a perovskite‐oxide‐based capacitor is evaluated considering the effect of the Ru and SrRuO3 top electrodes on the SrRuO3/Ba0.5Sr0.5TiO3 stack. Dead layers at the interface between each top electrode material and the dielectric are studied on the atomic scale. The results indicate that the Ru metal electrode causes oxygen to diffuse from the dielectric to the electrode, forming elongated perovskite oxide at the interface, which acts as a dead layer. However, minimizing the dead layer at the top interface increases the dielectric permittivity from 667 to 953. Consequently, the phenomenon and mechanism of the dead layer are intuitively identified. This study proposes a method to overcome the limitations of next‐generation dynamic random access memory (DRAM).

This paper is to explore the association between environmental, social, and governance (ESG) dimensions and corporate innovative activities. Given that existing literature in the relationship between ESG and firm performance to date offers no clearly consistent empirical results and large-scale sample studies based on global companies remain scarce, this paper aims to offer a fresh and noteworthy insight about relationship between the ESG and innovation in recent times when ESG has been considered as important tenets of management because of the ESG’s enormous significance for corporate solid performance and sound development of capital markets. To test the hypotheses derived from the theoretical reasoning, this paper selected ESG indicators and financial data of global companies from 1999 to 2009 based on the relationship between ESG activities and corporate innovation, and the effects on corporate innovative product development performance are analyzed according to detailed ESG activities by logistic regression analysis. The study found that positive environmental and social activities appear to enhance innovation outputs by firms in various industries. These results indicate that the firms challenged by environmental issues respond with assertive innovative activities and the firms’ efforts to stimulate certain types of social activities are reflected in a higher level of innovation. Supporting the complex relationship between ESG activities and corporate innovation performance, the findings will help firms’ decision-makers, stakeholders, as well as academics, to improve their awareness of the impact of ESG activities on the innovation performance of the firm, both as a comprehensive factor and individually by pillar.

Abstract Background Cognitive impairment, a characteristic and prior stage of dementia, is a serious public health concern in Korea a country with rapidly aging population. In a neurovisceral integration model, cognitive ability is connected to emotional and autonomic regulation via an interconnection in the brain, which may be associated with health-related quality of life (HRQoL). Methods This study investigated the association between the HRQoL and the autonomic nervous system (ANS) via EuroQoL-5 Dimension (EQ-5D) and heart rate variability (HRV) among 417 patients who visited the Neurology Department in Veterans Health Service Medical Center, Seoul, South Korea. Results The mean age of 275 patients in the cognitive impairment group (CIG) was higher than that of 142 patients in the normal cognition group (NCG) (74.85 years vs. 72.96 years, p

Abstract The stellar optoelectronic properties of metal halide perovskites provide enormous promise for next-generation optical devices with excellent conversion efficiencies and lower manufacturing costs. However, there is a long-standing ambiguity as to whether the perovskite surface/interface (e.g. structure, charge transfer or source of off-target recombination) or bulk properties are the more determining factor in device performance. Here we fabricate an array of CsPbI3 crystal and hybrid glass composites by sintering and globally visualise the property-performance landscape. Our findings reveal that the interface is the primary determinant of the crystal phases, optoelectronic quality, and stability of CsPbI3. In particular, the presence of a diffusion “alloying” layer is discovered to be critical for passivating surface traps, and beneficially altering the energy landscape of crystal phases. However, high-temperature sintering results in the promotion of a non-stoichiometric perovskite and excess traps at the interface, despite the short-range structure of halide is retained within the alloying layer. By shedding light on functional hetero-interfaces, our research offers the key factors for engineering high-performance perovskite devices.

Abstract This study aimed to develop and validate an automated machine learning (ML) system that predicts 3-month functional outcomes in acute ischemic stroke (AIS) patients by combining clinical and neuroimaging features. Functional outcomes were categorized as unfavorable (modified Rankin Scale ≥ 3) or not. A clinical model employing optimal clinical features (Model_A), a convolutional neural network model incorporating imaging data (Model_B), and an integrated model combining both imaging and clinical features (Model_C) were developed and tested to predict unfavorable outcomes. The developed models were compared with each other and with traditional risk-scoring models. The dataset comprised 4147 patients from a multicenter stroke registry, with 1268 (30.6%) experiencing unfavorable outcomes. Age, initial NIHSS, and early neurologic deterioration were identified as the most important clinical features. The ML model prediction achieved an area under the curves of 0.757 (95% CI 0.726–0.789) for Model_A, 0.725 (95% CI 0.693–0.755) for Model_B, and 0.786 (95% CI 0.757–0.814) for Model_C in the test set. The integrated models outperformed traditional risk-scoring models by 0.21 (95% CI 0.16–0.25) for HIAT and 0.15 (95% CI 0.11–0.19) for THRIVE. In conclusion, the integrated ML system enhanced stroke outcome prediction by combining imaging data and clinical features, outperforming traditional risk-scoring models.

While cool coatings have recently received much attention for building applications, their impact on building energy consumption strongly depends upon climatic conditions. Herein we evaluate the energy, cost, carbon, and interior comfort impact of cool coatings applied to a residential multifamily building across 32 climate zones in the United States by applying advanced cool coating properties to established building energy models. The model not only considers promising cool coating properties based upon recent experiments but also an ideal cool coating. Our calculations show that the ideal cool coating can achieve annual cooling energy savings of up to 6.64 kWh/m2 (Phoenix, AZ), annual net utility cost savings up to $1.16/m2 (Brawley, CA), and net annual carbon emission savings up to 7.7 % (Phoenix, AZ). We also estimate the change in interior temperature for buildings without space cooling systems and show that cool coatings make buildings in the warmest climate zones in the U.S. without space cooling more comfortable by 30 % to 50 % on a cooling degree days basis. Using analysis of variance, we examine the statistical relationships between building performance metrics and climatic parameters. The presented methodology enables evaluation of cool coating application to buildings in various climate zones across the world.

Abstract There has been an increasing demand for robotic coil positioning during repetitive transcranial magnetic stimulation (rTMS) treatment. Accurate coil positioning is crucial because rTMS generally targets specific brain regions for both research and clinical application with other reasons such as safety, consistency and reliability and individual variablity. Some previous studies have employed industrial robots or co-robots and showed they can more precisely stimulate the target cortical regions than traditional manual methods. In this study, we not only developed a custom-TMS robot for better TMS coil placement but also analyzed the therapeutic effects on depression. Treatment effects were evaluated by measuring regional cerebral blood flow (rCBF) using single-photon emission computed tomography and depression severity before and after rTMS for the two positioning methods. The rTMS preparation time with our robotic coil placement was reduced by 53% compared with that of the manual method. The position and orientation errors were also significantly reduced from 11.17 mm and 4.06° to 0.94 mm and 0.11°, respectively, confirming the superiority of robotic positioning. The results from clinical and neuroimaging assessments indicated comparable improvements in depression severity and rCBF in the left dorsolateral prefrontal cortex between the robotic and manual rTMS groups. A questionnaire was used to determine the patients’ feelings about the robotic system, including the safety and preparation time. A high safety score indicated good acceptability of robotic rTMS at the clinical site.

Highlights A comprehensive discussion of the recent advances in zinc–bromine rechargeable batteries with flow or non-flow electrolytes is presented. The fundamental electrochemical aspects including the key challenges and promising solutions in both zinc and bromine half-cells are reviewed. The key performance metrics of ZBRBs and assessment methods using various ex situ and in situ/operando techniques are also discussed.

Abstract Transition metal dichalcogenides (TMDs) have emerged as a promising alternative to noble metals in the field of electrocatalysts for the hydrogen evolution reaction. However, previous attempts using machine learning to predict TMD properties, such as catalytic activity, have been shown to have limitations in their dependence on large amounts of training data and massive computations. Herein, we propose a genetic descriptor search that efficiently identifies a set of descriptors through a genetic algorithm, without requiring intensive calculations. We conducted both quantitative and qualitative experiments on a total of 70 TMDs to predict hydrogen adsorption free energy ( $$\Delta G_H$$ Δ G H ) with the generated descriptors. The results demonstrate that the proposed method significantly outperformed the feature extraction methods that are currently widely used in machine learning applications.

Vehicular ad hoc networks (VANETs) use multiple channels to communicate using wireless access in vehicular environment (WAVE) standards to provide a variety of vehicle-related applications. The current IEEE 802.11p WAVE communication channel structure is composed of one control channel (CCH) and several service channels (SCHs). SCHs are used for non-safety data transmission, while the CCH is used for broadcasting beacons, control, and safety. WAVE devices transmit data that alternate between CCHs and SCHs, and each channel is active for a duration called the CCH interval (CCHI) and SCH interval (SCHI), respectively. Currently, both intervals are fixed at 50 ms. However, fixed-length intervals cannot effectively respond to dynamically changing traffic loads. Additionally, when many vehicles are simultaneously using the limited channel resources for data transmission, the network performance significantly degrades due to numerous packet collisions. Herein, we propose an adaptive resource allocation technique for efficient data transmission. The technique dynamically adjusts the SCHI and CCHI to improve network performance. Moreover, to reduce data collisions and optimize the network’s backoff distribution, the proposed scheme applies reinforcement learning (RL) to provide an intelligent channel access algorithm. The simulation results demonstrate that the proposed scheme can ensure high throughputs and low transmission delays.

Abstract Microstructures with engineered properties are critical to thermal management in aerospace and space applications. Due to the overwhelming number of microstructure design variables, traditional approaches to material optimization can have time-consuming processes and limited use cases. Here, we combine a surrogate optical neural network with an inverse neural network and dynamic post-processing to form an aggregated neural network inverse design process. Our surrogate network emulates finite-difference time-domain simulations (FDTD) by developing a relationship between the microstructure’s geometry, wavelength, discrete material properties, and the output optical properties. The surrogate optical solver works in tandem with an inverse neural network to predict a microstructure’s design properties that will match an input optical spectrum. As opposed to conventional approaches that are constrained by material selection, our network can identify new material properties that best optimize the input spectrum and match the output to an existing material. The output is evaluated using critical design constraints, simulated in FDTD, and used to retrain the surrogate—forming a self-learning loop. The presented framework is applicable to the inverse design of various optical microstructures, and the deep learning-derived approach will allow complex and user-constrained optimization for thermal radiation control in future aerospace and space systems.

Abstract We present a novel scheme to obtain robust, narrowband, and tunable THz emission using a nano-dimensional overdense plasma target, irradiated by two counter-propagating detuned laser pulses. So far, no narrowband THz sources with a field strength of GV/m-level have been reported from laser-solid interaction (mostly half-or single-cycle THz pulses with only broadband frequency spectrum). From two- and three-dimensional particle-in-cell simulations, we find that the strong plasma current generated by the beat ponderomotive force in the colliding region, produces beat-frequency radiation in the THz range. Here we report intense THz pulses $$(f\simeq 30 \;$$ ( f ≃ 30 THz) with an unprecedentedly high peak field strength of 11.9 GV/m and spectral width $$(\Delta f/f\simeq 5.3\%)$$ ( Δ f / f ≃ 5.3 % ) , which leads to a regime of an extremely bright narrowband THz source of TW/cm $$^{2}$$ 2 , suitable for various ambitious applications.

The purpose of this study is to analyze the direction of the Artificial Intelligence (AI) education policy announced by the South Korean government and how key tasks are reflected in detailed policies to find implications. The study first identified three major policy directions as found in the South Korean government’s publication on ‘Education Policy Directions and Core Tasks in the Era of AI’ and conducted a text mining analysis on government policy materials, press releases, and reports related to AI education from 2020 to 2022 by extracting keywords. According to our topic analysis results, various AI education contents were being developed both before and after the national curriculum revision. Moreover, our research indicates that the advancement and activation of AI education policies will take place to smoothly implement the curriculum in classrooms, and additional policies and challenges will emerge when the new curriculum comes into effect in 2025.

Abstract Capacitors based on ABO3-type perovskite oxides show considerable promise for overcoming the limitations of nanoscale integration with dynamic random access memory (DRAM) devices. Among the thermodynamically stable perovskite oxides, titanates (ATiO3) exhibit high dielectric permittivity in metal–insulator–metal (MIM) configurations. However, their performance in mitigating the large leakage current caused by their narrow bandgap (3 eV) remain under scrutiny. Herein, substantially enhanced dielectric properties of an epitaxial SrRuO3/Ba0.5Sr0.5TiO3/SrRuO3 MIM capacitor with a thin dielectric layer (10 nm) are reported. The dielectric/electrode heterointerface was engineered to realize a capacitor with a low leakage current and high dielectric permittivity. A pit-free and stoichiometric SrRuO3 bottom electrode with an atomically smooth surface was exploited to suppress defect formation at the heterointerface. The critical roles of oxygen vacancies and substituted transition-metal atoms in determining the leakage current were assessed, and a strategy for reducing the leakage current via interface engineering was established. Consequently, a dielectric permittivity of 861 and a leakage current density of 5.15 × 10−6 A/cm2 at 1 V were obtained with the thinnest dielectric layer ever reported. Our work paves the way for the development of perovskite-oxide-based capacitors in next-generation DRAM memories.

Recent advances in flexible sensors and wireless electronics have driven the development of lightweight and ergonomic wearable sensing gloves. Such gloves can be employed in mixed reality (MR) environments to give haptic capabilities during interactions with various objects. However, no prior study shows a quantitative measurement of physical user interactions of object manipulation in MR. Here, we report an MR-integrated soft bioelectronic system on a glove for quantifying the changes in the user's pinching tasks. We use nanomanufacturing techniques to fabricate flexible sensors, wireless circuits, and stretchable interconnectors seamlessly integrated with a wearable glove. The wearable biosensing glove with an integrated capacitive pressure sensor evaluates how users interact directly and indirectly interact with objects. The direct mode describes a user's direct touching and manipulating objects in MR. In contrast, in the indirect mode, objects are located far away and touched via a narrow light beam. The virtual object measurement parameters include mass, movement latency, dynamic friction coefficient, angular drag coefficient, and linear drag coefficient. The experimental results with human subjects show positive, linear relationships between pinching force and dynamic friction coefficient and mass parameters during the direct manipulation mode. Collectively, the MR-enabled wearable biosensing glove system offers unique advantages in detecting physical interactions and sensory feedback for various rehabilitation applications and MR human-machine interfaces.

BackgroundFunctional limitations and disabilities have been associated with a decrease in cognitive function due to increasing age. Gait performance and cognitive function have been associated with gait variability in executive function, the phase domain in memory, and gait abnormalities in cognitive decline. ObjectiveOur study aimed to investigate whether gait harmony was associated with cognitive function in the older adult population. Moreover, we aimed to investigate whether gait harmony was associated with cognitive function and explore each cognitive function in a specific harmonic state. MethodsThe study population included 510 adults aged ≥60 years who visited the Department of Neurology at the Veterans Health Service Medical Center, Seoul, South Korea. Gait data were collected using a 3D motion capture device with a wireless inertial measurement unit system. For cognitive function assessments, we used the Seoul Neuropsychological Screening Battery-Core test, which evaluates the level of cognitive function or impairment in 5 cognitive domains. ResultsIn general, the association between the Seoul Neuropsychological Screening Battery-Core tests and the stance-to-swing ratio in the >1.63 ratio group yielded lower β coefficients than those in the 1.50-1.63 ratio group. After adjustment for confounders, the odds ratio (OR) for the Digit Symbol Coding test (adjusted OR 0.42, 95% CI 0.20-0.88) and the Korean version of the Color Word Stroop Test: 60 seconds (adjusted OR 0.51, 95% CI 0.29-0.89) for frontal and executive function were significantly lower for the >1.63 ratio group than the reference group. ConclusionsOur findings suggest that the gait phase ratio is a valuable indicator of walking deficits and may also be associated with cognitive impairment in older adults.

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.

Abstract Background Klotho deficiency is a significant predictor of cardiovascular disease (CVD)-related mortality and morbidity. However, research assessing the association between klotho and individual risk factors of CVD is limited. This study aimed to explore the association between circulating serum klotho levels and risk factors for CVD in adults. Methods We used the 2007–2016 National Health and Nutrition Examination Survey and included 13,154 participants for whom serum klotho levels were available. Body mass index (BMI), exercise, smoking status, alcohol consumption, hypertension, dyslipidemia, serum lipid parameters, and blood pressure were considered as CVD risk factors. Results Circulating klotho levels were negatively associated with being overweight (beta coefficient: − 22.609, p = 0.0025), obesity (beta coefficient: − 23.716, p = 0.0011), current smoking (beta coefficient: − 46.412, p

Abstract Microstructured materials that can selectively control the optical properties are crucial for the development of thermal management systems in aerospace and space applications. However, due to the vast design space available for microstructures with varying material, wavelength, and temperature conditions relevant to thermal radiation, the microstructure design optimization becomes a very time-intensive process and with results for specific and limited conditions. Here, we develop a deep neural network to emulate the outputs of finite-difference time-domain simulations (FDTD). The network we show is the foundation of a machine learning based approach to microstructure design optimization for thermal radiation control. Our neural network differentiates materials using discrete inputs derived from the materials’ complex refractive index, enabling the model to build relationships between the microtexture’s geometry, wavelength, and material. Thus, material selection does not constrain our network and it is capable of accurately extrapolating optical properties for microstructures of materials not included in the training process. Our surrogate deep neural network can synthetically simulate over 1,000,000 distinct combinations of geometry, wavelength, temperature, and material in less than a minute, representing a speed increase of over 8 orders of magnitude compared to typical FDTD simulations. This speed enables us to perform sweeping thermal-optical optimizations rapidly to design advanced passive cooling or heating systems. The deep learning-based approach enables complex thermal and optical studies that would be impossible with conventional simulations and our network design can be used to effectively replace optical simulations for other microstructures.

Abstract This study uncovers a correlation between the mid-infrared emissivity of butterfly wings and the average air temperature of their habitats across the world. Butterflies from cooler climates have a lower mid-infrared emissivity, which limits heat losses to surroundings, and butterflies from warmer climates have a higher mid-infrared emissivity, which enhances radiative cooling. The mid-infrared emissivity showed no correlation with other investigated climatic factors. Phylogenetic independent contrasts analysis indicates the microstructures of butterfly wings may have evolved in part to regulate mid-infrared emissivity as an adaptation to climate, rather than as phylogenetic inertia. Our findings offer new insights into the role of microstructures in thermoregulation and suggest both evolutionary and physical constraints to butterflies’ abilities to adapt to climate change.

Abstract Rechargeable aqueous zinc ion hybrid capacitors (ZIHCs), as an up‐and‐comer aqueous electrochemical energy storage system, endure in their infancy because of the substandard reversibility of Zn anodes, structural deterioration of cathode materials, and narrow electrochemical stability window. Herein, a scalable approach is described that addresses Zn‐anode/electrolyte interface and cathode materials associated deficiencies and boosts the electrochemical properties of ZIHCs. The Zn‐anode/electrolyte interface is self‐regulated by alteration of the traditional Zn2+ electrolyte with Na‐based supporting salt without surrendering the cost, safety, and green features of the Zn‐based system which further validates the excellent reversibility over 1100 h with suppressed hydrogen evolution. The deficits of cathode materials were overcome by using a high‐mass loaded, oxygen‐rich, 3D, multiscaled graphene‐like carbon (3D MGC) cathode. Due to the multiscaled texture, high electronic conductivity, and oxygen‐rich functional groups of 3D MGC, reversible redox capacitance was obtained with a traditional adsorption/desorption mechanism. Prototype ZIHCs containing the modified electrolyte and an oxygen‐rich 3D MGC cathode resulted in battery‐like specific energy (203 Wh kg−1 at 1.6 A g−1) and supercapacitor‐type power capability (4.9 kW kg−1 at 8 A g−1) with outstanding cycling durability (96.75% retention over 30 000 cycles at 10 A g−1). These findings pave the way toward the utilization of highly efficient ZIHCs for practical applications.

Abstract Although light flickering at 40 Hz reduced Alzheimer’s disease (AD) pathologies in mice by entraining gamma waves, it failed to reduce cerebral amyloid burden in a study on six patients with AD or mild cognitive impairment. We investigated the optimal color, intensity, and frequency of the flickering light stimulus for entraining gamma waves in young adults. We compared the event-related synchronization (ERS) values of entrained gamma waves between four different light colors (white, red, green, and blue) in the first experiment and four different luminance intensities in the second experiment. In both experiments, we compared the ERS values of entrained gamma waves between 10 different flickering frequencies from 32 to 50 Hz. We also examined the severity of six adverse effects in both experiments. We compared the propagation of gamma waves in the visual cortex to other brain regions between different luminance intensities and flickering frequencies. We found that red light entrained gamma waves most effectively, followed by white light. Lights of higher luminance intensities (700 and 400 cd/m2) entrained stronger gamma waves than those of lower luminance intensities (100 and 10 cd/m2). Lights flickering at 34–38 Hz entrained stronger and more widely spread beyond the visual cortex than those flickering at 40–50 Hz. Light of 700 cd/m2 resulted in more moderate-to-severe adverse effects than those of other luminance intensities. In humans, 400 cd/m2 white light flickering at 34–38 Hz was most optimal for gamma entrainment.

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.

Wireless sensor networks (WSNs) have been utilized as communication infrastructure for smart grid applications. The primary requirement of WSNs for smart grid applications is to transmit delay-critical data from smart grid assets ether at the maximum rate or by reducing collision rates. Additionally, WSNs should utilize the limited resources of the network to provide the required long-term QoS. The achievement of these objectives requires a remarkable design of WSN protocols to satisfy the requirements of smart grid applications. In this study, a multi-channel cluster tree protocol is proposed to prevent collisions and increase network performance. In the proposed scheme, the cluster head serves to broadcast a beacon frame containing information on the allocated channels and time slots. This enables the new node to determine its channel and timeslot. A performance analysis reveals that the proposed scheme can achieve a low end-to-end delay and low collision rates compared with the well-known IEEE 802.15.4 MAC protocols widely used in the literature to provide QoS to smart-grid applications.

In indoor environments, estimating localization using a received signal strength indicator (RSSI) is difficult because of the noise from signals reflected and refracted by walls and obstacles. In this study, we used a denoising autoencoder (DAE) to remove noise in the RSSI of Bluetooth Low Energy (BLE) signals to improve localization performance. In addition, it is known that the signal of an RSSI can be exponentially aggravated when the noise is increased proportionally to the square of the distance increment. Based on the problem, to effectively remove the noise by adapting this characteristic, we proposed adaptive noise generation schemes to train the DAE model to reflect the characteristics in which the signal-to-noise ratio (SNR) considerably increases as the distance between the terminal and beacon increases. We compared the model’s performance with that of Gaussian noise and other localization algorithms. The results showed an accuracy of 72.6%, a 10.2% improvement over the model with Gaussian noise. Furthermore, our model outperformed the Kalman filter in terms of denoising.

We investigated the prognostic significance of radiomic features from 18F-FDG PET/CT to predict overall survival (OS) in patients with stage III NSCLC undergoing neoadjuvant chemoradiation therapy followed by surgery. We enrolled 300 patients with stage III NSCLC who underwent PET/CT at the initial work-up (PET1) and after neoadjuvant concurrent chemoradiotherapy (PET2). Radiomic primary tumor features were subjected to LASSO regression to select the most useful prognostic features of OS. The prognostic significance of the LASSO score and conventional PET parameters was assessed by Cox proportional hazards regression analysis. In conventional PET parameters, metabolic tumor volume (MTV) and total lesion glycolysis (TLG) of each PET1 and PET2 were significantly associated with OS. In addition, both the PET1-LASSO score and the PET2-LASSO score were significantly associated with OS. In multivariate Cox regression analysis, only the PET2-LASSO score was an independently significant factor for OS. The LASSO score showed better predictive performance for OS regarding the time-dependent receiver operating characteristic curve and decision curve analysis than conventional PET parameters. Radiomic features from PET/CT were an independent prognostic factor for the estimation of OS in stage III NSCLC. The newly developed LASSO score using radiomic features showed better prognostic results for individualized OS estimation than conventional PET parameters.

Although numerous schemes, including learning-based approaches, have attempted to determine a solution for location recognition in indoor environments using RSSI, they suffer from the severe instability of RSSI. Compared with the solutions obtained by recurrent-approached neural networks, various state-of-the-art solutions have been obtained using the convolutional neural network (CNN) approach based on feature extraction considering indoor conditions. Complying with such a stream, this study presents the image transformation scheme for the reasonable outcomes in CNN, obtained from practical RSSI with artificial Gaussian noise injection. Additionally, it presents an appropriate learning model with consideration of the characteristics of time series data. For the evaluation, a testbed is constructed, the practical raw RSSI is applied after the learning process, and the performance is evaluated with results of about 46.2% enhancement compared to the method employing only CNN.

This paper presents a novel adaptive learning-rate backpropagation neural network (ALR-BPNN) algorithm based on the minimization of mean-square deviation (MSD) to implement a fast convergence rate and robustness to impulsive noises. The learning rates of the weights in each hidden layer are derived to minimize the upper bound of the MSD obtained by the analysis, which guarantees a fast convergence rate in a stable range. Moreover, by adopting the variance of the kind of the measurement noises in each layer through the variance of the error signals, the proposed scheme provides robustness to the impulsive noises. The performance of the proposed algorithm is evaluated on various sequential signals and industrial data including the impulsive noise and compared with conventional ALR-BPNN algorithms. Simulation results indicate that the proposed algorithm outperforms the existing algorithms.

With the emergence of urban computing technology, the development of smart cities has gained much attention as a means to improve citizens' quality of life. As traffic accidents constitute a major problem that affects the quality of life, an effective solution to address this problem can significantly increase the level of intelligence of smart cities. This paper presents the development of a mathematical model for accurate analysis of big data to promote the effectiveness of policy decisions, thereby largely advancing the intelligent transportation systems (ITS) of smart cities. Temporal impulse was designed as a novel and measurable quantity to analyze traffic accidents by identifying the hidden patterns, such as varying causes and diverging impacts of traffic accidents. Based on the big data produced by the South Korean National Police Agency, we analyzed traffic accidents over three years by applying the temporal impulse. The research results suggested that the temporal impulse not only helped in identifying the varying influence of weather and driver conditions but also facilitated the establishment of sophisticated policies in the implementation of smart cities with the use of urban computing technology. As presented in the section VII, our simulation outputs indicated that our temporal model was predictive within the parameter space comprising driver's dynamic behaviors, day of the week, and environmental factors including weather, road surface condition, and road type.

Abstract Understanding and designing interfacial band alignment in a molecular heterojunction provides a foundation for realizing its desirable electronic functionality. In this study, a tailored molecular heterojunction selector is implemented by controlling its interfacial band offset between the molecular self‐assembled monolayer with opposite dipole orientations and the 2D semiconductor (1L‐MoS2 or 1L‐WSe2). The molecular dipole moment direction determines the direction of the band bending of the 2D semiconductors, affecting the dominant transport pathways upon voltage application. Notably, in the molecular heterostructure with 1L‐WSe2, the opposite rectification direction is observed depending on the molecular dipole moment direction, which does not hold for the case with 1L‐MoS2. In addition, the nonlinearity of the molecular heterojunction selector can be significantly affected by the molecular dipole moment direction, type of 2D semiconductor, and metal work function. According to the choice of these heterojunction constituents, the nonlinearity is widely tuned from 1.0 × 101 to 3.6 × 104 for the read voltage scheme and from 0.4 × 101 to 2.0 × 105 for the half‐read voltage scheme, which can be scaled up to an ≈482 Gbit crossbar array.

The long-non-coding HOX transcript antisense intergenic RNA (HOTAIR) was identified as significantly upregulated in breast ductal carcinoma in situ (DCIS). The aim of this study was to characterize the phenotypic effects and signaling pathways modulated by HOTAIR in early-stage breast cancer progression. We determined that HOTAIR induces premalignant phenotypic changes by increasing cell proliferation, migration, invasion and in vivo growth in normal and DCIS breast cell lines. Transcriptomic studies (RNA-seq) identified the main signaling pathways modulated by HOTAIR which include bioprocesses related to epithelial to mesenchymal transition, cell migration, extracellular matrix remodeling and activation of several signaling pathways (HIF1A, AP1 and FGFR). Similar pathways were identified as activated in primary invasive breast carcinomas with HOTAIR over-expression. We conclude that HOTAIR over-expression behaves as a positive regulator of cell growth and migration both in normal and DCIS breast cells involved with early-stage breast cancer progression.

The IEEE 802.15.4 standard is recognized as one of the most successful for short-range low-rate wireless communications and is used in Internet of Things (IoT) applications. To improve the performance of wireless networks, interest in protocols that rely on interaction between different layers has increased. Cross-layer design has become an issue in wireless communication systems as it can improve the capacity of wireless networks by optimizing cooperation between multiple layers that constitute network systems. Power efficiency and network scalability must be addressed to spread IoT. In multi-hop networks, many devices share wireless media and are geographically distributed; consequently, efficient medium access control (MAC) and routing protocols are required to mitigate interference and improve reliability. Cross-layer design is a novel network design approach to support flexible layer techniques in IoT. We propose a cross-layer protocol for the MAC layer and routing layer to satisfy the requirements of various networks. The proposed scheme enables scalable and reliable mesh networking using the IEEE 802.15.4 standard and provides robust connectivity and efficient path discovery procedures. It also proposes a novel address-allocation technique to improve address-allocation methods that cannot support large sensor networks. Simulation results indicate that the proposed scheme could improve reliability and reduce end-to-end delay.

A smart grid is a next-generation intelligent power grid that can maximize energy efficiency by monitoring power information in real time and by controlling the flow of power by introducing IT communication technology to the existing power grid. In order to apply a wireless communication network to a smart grid, it is necessary to be able to efficiently process large amounts of power-related data while enabling a high level of reliability and quality of service (QoS) support. In addition, international standards-based design is essential considering compatibility and scalability. The IEEE 802.15.4 standard is considered to be the most powerful communication method for processing data through the smart grid AMI. To reduce the energy consumption, as the duty cycle of the superframe increases, the probability of the congestion increases. However, this binary exponential algorithm in IEEE 802.15.4 standard does not account for the application of traffic characteristics that essentially negatively affect the smart grid network performances in terms of packet delivery ratio and time delay. Therefore, in this paper, we propose a new transmission scheme to reduce performance degradation by excessive collisions in the content access period (CAP), when data transmission is performed in IEEE 802.15.4 applied to smart grids. In addition, we investigated the main research topics required when applying wireless networking technology to smart grids and suggested improvement measures. Simulation results showed that the proposed scheme increased the data delivery rate and reduced the latency, and it was confirmed that reliability was improved.

Abstract In this study, we synthesized molecularly imprinted mesoporous organosilica (MIMO) in the presence of a new precursor having a zwitterionic functional group and an imprint molecule, namely, glyphosate (MIMO-z). The precursor-glyphosate complex engaged in a typical base-catalyzed sol-gel reaction and the introduced zwitterion group remained intact in the framework after the extraction process had been completed. To test the rebinding performance of the target molecule, graphene quantum dots were encapsulated (MIMO-zQ) into pores and the fluorescence intensity change was monitored according to the concentration of glyphosate. When the MIMO-zQ suspension was diluted into the glyphosate solutions, notable fluorescence quenching occurred, right down to sub-nanomolar levels of concentration; 9.2 ± 0.18% quenching at 0.1 nM (0.017 ppb, 17 pg/mL). This result is one of the best reported to date for sensing using MIMO. The synthesized probe also exhibited a distinct signal compared to a series of competing compounds, aminomethylphosphonic acid and glycine; 4.3 ± 0.019% and 3.7 ± 0.041% quenching at 100 nM.

Burcucumber (Sicyos angluatus) is a representative ecosystem-disturbing plant in Korea and currently widely spread throughout the country. A sclerotia-forming fungus with moderate host selectivity, Sclerotinia trifoliorum BWC98-105, was tested in the laboratory, green house and natural habitat for its pathogenicity to burcucumber. When mycelial culture fragment was inoculated to burcucumber seedlings under the green house condition, mycelial growth was observed in the following day, and then resulted in the onset of wilting from 5 days after inoculation (DAI). Its characteristic sclerotia as a sign was observed from 7 DAI, and thus plants turned into dark-brown color at the bottom of stem of burcucumber that was eventually blighted at 14 DAI. Similar visible symptoms were observed in natural habitat. Based on the results of showing typical blight symptom to burcucumber and the sign of sclerotia, we report S. trifoliorum BWC98-105 causing stem blight against burcucumber. Its globular pellet was considered of having quite potential as a bioherbicide to control burcucumber in Korea.

The association of WW domain-containing oxidoreductase WWOX gene loss of function with central nervous system (CNS) related pathologies is well documented. These include spinocerebellar ataxia, epilepsy and mental retardation (SCAR12, OMIM: 614322) and early infantile epileptic encephalopathy (EIEE28, OMIM: 616211) syndromes. However, there is complete lack of understanding of the pathophysiological mechanisms at play. In this study, using a Wwox knockout (Wwox KO) mouse model (2 weeks old, both sexes) and stereological studies we observe that Wwox deletion leads to a significant reduction in the number of hippocampal GABA-ergic (γ-aminobutyric acid) interneurons. Wwox KO mice displayed significantly reduced numbers of calcium-binding protein parvalbumin (PV) and neuropeptide Y (NPY) expressing interneurons in different subfields of the hippocampus in comparison to Wwox wild-type (WT) mice. We also detected decreased levels of Glutamic Acid Decarboxylase protein isoforms GAD65/67 expression in Wwox null hippocampi suggesting lower levels of GABA synthesis. In addition, Wwox deficiency was associated with signs of neuroinflammation such as evidence of activated microglia, astrogliosis, and overexpression of inflammatory cytokines Tnf-a and Il6. We also performed comparative transcriptome-wide expression analyses of neural stem cells grown as neurospheres from hippocampi of Wwox KO and WT mice thus identifying 283 genes significantly dysregulated in their expression. Functional annotation of transcriptome profiling differences identified ‘neurological disease’ and ‘CNS development related functions’ to be significantly enriched. Several epilepsy-related genes were found differentially expressed in Wwox KO neurospheres. This study provides the first genotype-phenotype observations as well as potential mechanistic clues associated with Wwox loss of function in the brain.

Long non-coding RNAs are increasingly being recognized as cancer biomarkers in various malignancies, acting as either tumor suppressors or oncogenes. The long non-coding MALINC1 intergenic RNA was identified as significantly upregulated in breast ductal carcinoma in situ. The aim of this study was to characterize MALINC1 expression, localization, and phenotypic and molecular effects in non-invasive and invasive breast cancer cells. We determined that MALINC1 is an estrogen–estrogen receptor-modulated lncRNA enriched in the cytoplasmic fraction of luminal A/B breast cancer cells that is associated with worse overall survival in patients with primary invasive breast carcinomas. Transcriptomic studies in normal and DCIS cells identified the main signaling pathways modulated by MALINC1, which mainly involve bioprocesses related to innate and adaptive immune responses, extracellular matrix remodeling, cell adhesion, and activation of AP-1 signaling pathway. We determined that MALINC1 induces premalignant phenotypic changes by increasing cell migration in normal breast cells. Moreover, high MALINC1 expression in invasive carcinomas was associated with a pro-tumorigenic immune environment and a favorable predicted response to immunotherapy both in luminal and basal-like subtypes compared with low-MALINC1-expression tumors. We conclude that MALINC1 behaves as an oncogenic and immune-related lncRNA involved with early-stage breast cancer progression.

We investigated predictions from 18F-FDG PET/CT using machine learning (ML) to assess the neoadjuvant CCRT response of patients with stage III non-small cell lung cancer (NSCLC) and compared them with predictions from conventional PET parameters and from physicians. A retrospective study was conducted of 430 patients. They underwent 18F-FDG PET/CT before initial treatment and after neoadjuvant CCRT followed by curative surgery. We analyzed texture features from segmented tumors and reviewed the pathologic response. The ML model employed a random forest and was used to classify the binary outcome of the pathological complete response (pCR). The predictive accuracy of the ML model for the pCR was 93.4%. The accuracy of predicting pCR using the conventional PET parameters was up to 70.9%, and the accuracy of the physicians’ assessment was 80.5%. The accuracy of the prediction from the ML model was significantly higher than those derived from conventional PET parameters and provided by physicians (p < 0.05). The ML model is useful for predicting pCR after neoadjuvant CCRT, which showed a higher predictive accuracy than those achieved from conventional PET parameters and from physicians.

Due to the growth of the internet and communication technologies, electronic financial systems are becoming popular. Physical cash is losing its preeminence, and digital numbers on computers represent money. However, electronic financial systems, mostly operated by private entities, have defects to be compensated for, such as high charges for using the system, security issues, and the problem of exclusion. As a solution, many countries around the world are considering central bank digital currency. For central bank digital currency to be utilized as a national legal tender, it must be universal and accessible regardless of time and place, similar to physical cash. Therefore, offline payment functions that extend the accessibility of central bank digital currency are becoming attractive. However, due to the characteristics of the electronic financial system, central bank digital currency is vulnerable to possible malicious behaviors in offline situations, such as blackouts and system shutdowns. This paper reviews research studies that deal with security matters related to the offline payment function of central bank digital currency. Offline payment solutions, including central bank digital currency and other electronic financial systems, such as electronic cash and cryptocurrency, are reviewed, and supplemental methods to improve the offline payment solutions of central bank digital currency based on trusted execution environment devices are suggested.

The circulatory neutrophil and brain tissue-resident microglia are two important immune cells involved in neuroinflammation. Since neutrophils that infiltrate through the brain vascular vessel may affect the immune function of microglia in the brain, close investigation of the interaction between these cells is important in understanding neuroinflammatory phenomena and immunological aftermaths that follow. This study aimed to observe how morphology and function of both neutrophils and microglia are converted in the inflamed brain. To directly investigate cellular responses of neutrophils and microglia, LysMGFP/+ and CX3CR1GFP/+ mice were used for the observation of neutrophils and microglia, respectively. In addition, low-dose lipopolysaccharide (LPS) was utilized to induce acute inflammation in the central nervous system (CNS) of mice. Real-time observation on mice brain undergoing neuroinflammation via two-photon intravital microscopy revealed various changes in neutrophils and microglia; namely, neutrophil infiltration and movement within the brain tissue increased, while microglia displayed morphological changes suggesting an activated state. Furthermore, neutrophils seemed to not only actively interact with microglial processes but also exhibit reverse transendothelial migration (rTEM) back to the bloodstream. Thus, it may be postulated that, through crosstalk with neutrophils, macrophages are primed to initiate a neuroinflammatory immune response; also, during pathogenic events in the brain, neutrophils that engage in rTEM may deliver proinflammatory signals to peripheral organs outside the brain. Taken together, these results both show that neuroinflammation results in significant alterations in neutrophils and microglia and lay the pavement for further studies on the molecular mechanisms behind such changes.

The purpose of this study is to examine and identify effective variables related to single-subject reading interventions for the struggling readers. This study conducted meta-analysis of 84 single-subject research articles of which 54 were published in Korean and 30 were published in English. Utilizing the Improvement Rate Difference (IRD), a total of 586 effect sizes were produced with the mean effect size of 0.77. Intervention variables were categorized into two types – participant-related and intervention-related variables. Collected studies were analyzed for each effect size calculation. Variables having large effects within grade levels, disability types, implementer and session duration are discerned along with further discussion and educational implications.

Objectives This study aimed to examine the effect of underlying individual preferences for the present over that for the future on lung cancer screening participation.Setting We analysed the data from the Korean National Cancer Screening Survey in 2018.Participants 4500 adults aged 20–74 years old participated in the study.Design In this cross-sectional survey, multivariate logistic regression analysis was carried out to examine the association between subjects’ intention to participate in lung cancer screening and individual preferences. The underlying individual preferences were measured on the basis of the self-reported general willingness to spend money now in order to save money in the future and general preferences with regard to financial planning.Primary outcome measure Intention to participate in lung cancer screening.Results Individuals eligible for lung cancer screening who place less value on their future were around four times less likely to report an intention to participate in lung cancer screening than were those who valued their future (OR 3.86, 95% CI 1.89 to 7.90). A present-biassed individual (one with a tendency for immediate gratification) was also about four times less likely to report an intention to participate in screening than an individual with no present bias (OR 0.26, 95% CI 0.12 to 0.57).Conclusions Underlying individual preferences regarding the present and future significantly affect individuals’ intention to participate in lung cancer screening. Hence, provision of incentives may be necessary to encourage the targeted heavy smokers who may have a strong preferences for the present over future.

While over the past decade architected cellular materials have been shown to possess unique mechanical properties, their thermal properties have received relatively little attention. Here, we investigate thermal transport in hollow nickel microlattices as a function of temperature and mechanical loading using infrared thermography. The effective thermal conductivity of hollow nickel microlattices with 99.9% porosity and 1 µm layer thickness is as low as 0.049 W m−1 K−1 at 320 K and increases to 0.075 W m−1 K−1 at 480 K, an increase we attribute to internal thermal radiation. By measuring the emissivity and using the Stephan-Boltzmann law, we estimate the contribution of thermal radiation in the effective thermal conductivity to range from 20% at 320 K to 49% at 480 K. The high porosity of microlattices strongly limits solid conduction and makes surface radiation very important in thermal transport. We further explore the impact of the strut surface condition by comparing hollow doped nickel microlattices with a smooth surface to those with a rough surface: the emissivity increases from 0.24 to 0.43, leading to increased thermal radiation contributions of 41% at 320 K to 58% at 480 K. Under mechanical loading, as the strain increases from 0% to 50%, decreasing the angle between the struts and the horizontal plane from 60° to 38°, the effective thermal conductivity decreases from 0.049 W m−1 K−1 to 0.016 W m−1 K−1. These findings indicate that architected cellular materials provide an excellent platform to control thermal properties independently on mechanical properties and to potentially develop thermal and thermomechanical metamaterials.

(1) Background: Previous studies have suggested the association between lipid profiles and cognitive function in older adults. However, they generated inconsistent results. We aim to determine the relationship between lipid profiles and cognitive performance in older adults. (2) Methods: We used the 2011–2014 National Health and Nutrition Examination Survey. This study included 2215 participants who were aged more than 60 years old and free of coronary heart disease or stroke. Lipid profiles included total cholesterol (TC), low density lipid cholesterol (LDL), high density lipid cholesterol (HDL), and triglyceride (TG). Cognitive function was assessed using the digit symbol substitution test (DSST). (3) Results: Positive correlations of DSST were observed with TC (r = 0.111; p < 0.0001), HDL (r = 0.127; p < 0.0001), and LDL (r = 0.107; p = 0.0005). However, there was no significant relationship between TG and DSST. A one-unit increase in HDL was associated with an increase in DSST score (beta coefficient: 0.036; p = 0.018); but the association was not significant for LDL, TG, and TC. In the categorical analysis, the high HDL group had a higher DSST score than the low HDL group (beta = 3.113; p < 0.0001) and the low TG group was more likely to show a lower DSST score than the high TC group (beta = −1.837; p = 0.0461). However, LDL and TC showed no statistically significant associations. Moreover, HDL was only associated with a 0.701 times increased risk of cognitive impairment (95% CI = 0.523–0.938) in the logistic regression analysis. (4) Conclusions: Higher blood concentrations of HDL levels were positively associated with DSST scores in older adults. We suggest that the high levels of HDL may be a protective factor against cognitive impairment.

Multi-person pose estimation has been gaining considerable interest due to its use in several real-world applications, such as activity recognition, motion capture, and augmented reality. Although the improvement of the accuracy and speed of multi-person pose estimation techniques has been recently studied, limitations still exist in balancing these two aspects. In this paper, a novel knowledge distilled lightweight top-down pose network (KDLPN) is proposed that balances computational complexity and accuracy. For the first time in multi-person pose estimation, a network that reduces computational complexity by applying a “Pelee” structure and shuffles pixels in the dense upsampling convolution layer to reduce the number of channels is presented. Furthermore, to prevent performance degradation because of the reduced computational complexity, knowledge distillation is applied to establish the pose estimation network as a teacher network. The method performance is evaluated on the MSCOCO dataset. Experimental results demonstrate that our KDLPN network significantly reduces 95% of the parameters required by state-of-the-art methods with minimal performance degradation. Moreover, our method is compared with other pose estimation methods to substantiate the importance of computational complexity reduction and its effectiveness.

The Rect-spring appliance, used for the management of ectopically erupting molars, shows weak retention on mesially tilted molars. We present three modifications of the appliance for better engagement and their advantages. We describe cases of two 7-year-old patients with ectopically erupting maxillary first molars with a 2.2 mm and 2.5 mm depth of entrapment, respectively. The modified Rect-spring (mRS) was inserted between the ectopically erupting first molar and adjacent primary second molar, and exerted a distalization force with an interproximal wedging effect at the same time. After 3 months, the ectopically erupting first molars were successfully brought into proper occlusion. No discomfort was reported. The mRS is suitable for various locking cases except for severely tilted molars without requiring any laboratory procedures. We suggest it as the first choice for unlocking the first molars.