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Background Remimazolam, an ultra-short-acting anesthetic with flumazenil as a reversal agent, typically facilitates patient awakening postoperatively. However, our case reveals an unusual occurrence: despite flumazenil initially restoring consciousness, re-sedation due to remimazolam ensued six hours later. Case A 65-year-old woman underwent total intravenous general anesthesia with remimazolam and remifentanil during the 140-min surgery. Despite an initially smooth recovery, she progressively became drowsy upon transfer to the general ward, eventually reaching a stuporous state. Multiple interventions, including opioid reversal (intravenous patient-controlled analgesia discontinuation, and naloxone administration) were attempted. Neurological consultation revealed no issues; however, immediate improvement after flumazenil administration suggested remimazolam's involvement. The patient was discharged without complications. Conclusions This case challenges our understanding of remimazolam's dynamics, emphasizing the necessity for vigilant post-anesthesia monitoring, even in seemingly low-risk cases. It advocates for standardized response protocols to promptly manage unforeseen events and ensure patient safety.

Abstract We propose a phoxonic cavity with structural hierarchy to enhance acousto-optic interaction in acoustically dissipative media. In a conventional phoxonic cavity, interaction between infrared light and hypersound with the same wavelength scale became weak due to large acoustic attenuation whose coefficient is proportional to the square of the frequency. To alleviate the acoustic attenuation, it is necessary to use low-frequency sound with much longer wavelength than the infrared light, but the conventional phoxonic cavity is not suitable for confining such hypersound and infrared light simultaneously. In this study, we employ the concept of structural hierarchy into the phoxonic cavity to control infrared light and hypersound with different wavelength scales. A phoxonic cavity with two different scales achieves the acousto-optic interaction approximately 1.6 times that in the conventional one. To further enhance the interaction, we adjust geometrical constitution and material properties of the two-scale phoxonic cavity using quasi-static homogenization theory, leading to the interaction about 2.1 times that in the conventional cavity.

Korea operates exclusive bus lanes (XBLs) on many of its expressways. As XBLs convert one lane of regular-use traffic into bus-only traffic, they have a large impact on traffic flow, so careful judgment is required to determine if the operation is effective. However, XBLs have been operated based on only political judgment due to the lack of standards for the operation of expressway XBLs. Therefore, we sought to establish standards for the operation of expressway XBLs using the micro-traffic simulation program VISSIM and the multi-criteria value function methodology. Various scenarios were established based on traffic volume changes of passenger vehicles and bus traffic of four- and five-lane expressway networks. Through an expert survey, the average speed and speed-deviation values, which are the criteria for evaluating operational efficiency and safety, were determined. Also, value points were converted using average speed and speed deviation extracted from the simulation. In addition, quantitative operation standards were established using the converted value scores. Using the results of this study, we established standards for the operation of the XBLs and presented guidelines for related agencies such as police, bus groups, and corporations. The National Police have prepared guidelines for the operation of the XBLs. Citing the results of this study, the new guidelines were implemented in February 2021, and sections of some XBLs have been abolished. Through this study, quantitative standards for the operation of XBLs, one of the management lane techniques necessary for sustainable highway operation, were prepared and applied to actual highways. By properly applying the newly applied guidelines according to quantitative standards, there will be effects of reducing traffic congestion, improving travel time, and enhancing environmental characteristics such as exhaust gas emission. It is also expected to have a positive effect on safety.

The increasing computational demands of Deep Reinforcement Learning (DRL) models, particularly for embedded systems in autonomous vehicles and drones, present significant challenges owing to their extensive neural network complexities. Previous DRL compression strategies predominantly focused on unstructured pruning, effective for reducing model size but requiring specialized hardware for computational acceleration. Conversely, DRL models with structured pruning applied can be accelerated on standard hardware, though they typically encounter performance issues at higher pruning rates due to structural constraints. In response to these challenges, this paper introduces an advanced structured pruning methodology, combined with scaled policy constraints (SPC) for DRL models. Our approach overcomes the performance limitations of conventional structured pruning, achieving high pruning rates while maintaining robust model performance. Enhanced performance restoration after pruning is achieved by fine-tuning with SPC and applying structural regularization, thus ensuring efficient decision-making with a minimal computational burden. Extensive evaluations on the D4RL benchmark and in a drone control simulation environment confirm the effectiveness of our method. Our approach maintains performance integrity even at high pruning rates, with less than a 2% decrease in normalized score at 90% pruning in D4RL and preserving cumulative reward at 87% pruning in drone control simulation. Significantly, our approach also enables considerable computational acceleration on standard hardware. We implemented our method on the NVIDIA Jetson Xavier NX board and achieved a 2.5-fold speed-up on devices with NVIDIA Volta GPUs and over double the speed-up on those with NVIDIA Carmel ARMv8.2 CPUs. These outcomes highlight our method’s suitability for real-time, resource-constrained applications, demonstrating its practicality and efficiency.

As network technology evolves, cyberattacks are not only increasing in frequency but also becoming more sophisticated. To proactively detect and prevent these cyberattacks, researchers are developing intrusion detection systems (IDSs) leveraging machine learning and deep learning techniques. However, a significant challenge with these advanced models is the increased training time as model complexity grows, and the symmetry between performance and training time must be taken into account. To address this issue, this study proposes a fast-persistent-contrastive-divergence-based deep belief network (FPCD-DBN) that offers both high accuracy and rapid training times. This model combines the efficiency of contrastive divergence with the powerful feature extraction capabilities of deep belief networks. While traditional deep belief networks use a contrastive divergence (CD) algorithm, the FPCD algorithm improves the performance of the model by passing the results of each detection layer to the next layer. In addition, the mix of parameter updates using fast weights and continuous chains makes the model fast and accurate. The performance of the proposed FPCD-DBN model was evaluated on several benchmark datasets, including NSL-KDD, UNSW-NB15, and CIC-IDS-2017. As a result, the proposed method proved to be a viable solution as the model performed well with an accuracy of 89.4% and an F1 score of 89.7%. By achieving superior performance across multiple datasets, the approach shows great potential for enhancing network security and providing a robust defense against evolving cyber threats.

Compact and robust frequency-stabilized laser sources are critical for a variety of fields that require stable frequency standards, including field spectroscopy, radio astronomy, microwave generation, and geophysical monitoring. In this work, we applied a simple and compact fiber ring-resonator configuration that can stabilize both a continuous-wave laser and a self-referenced optical frequency comb to a vibration-insensitive optical fiber delay-line. We could achieve a thermal-noise-limited frequency noise level in the 10 Hz–1 kHz offset frequency range for both the continuous-wave laser and the optical frequency comb with the minimal frequency instability of 2.7 × 10−14 at 0.03-s and 2.6 × 10−14 at 0.01-s averaging time, respectively, under non-vacuum conditions. The optical fiber spool, working as a delay reference, is designed to be insensitive to external vibrations, with a vibration sensitivity of sub-10−10 (1/g) and a volume of 32 ml. Finally, the ring-resonator setup is packaged in a palm-sized aluminum case with 171-ml volume with a vibration-insensitive spool, as well as an even smaller 97-ml-volume case with an ultracompact 9-ml miniaturized fiber spool.

Background Difficult airway occurs due to anatomical abnormalities of the airway that can be predicted through airway assessments; however, abnormalities beyond the vocal cord can be clinically asymptomatic and undetected until intubation failure to advance the endotracheal tube. Case We present a case of an unanticipated difficult airway in a stuporous 80-year-old female with a recent history of intracerebral hemorrhage and prolonged intubation. She required emergency ventriculo-peritoneal shunt surgery due to the progression of her hydrocephalus. Under anesthesia, facemask ventilation was easy and video laryngoscopy provided a full view of the glottis; however, endotracheal tube (ETT) entry failed. We suspected stenosis beyond the vocal cord, and a smaller diameter ETT was inserted and maintained for airway management during emergency surgery. Postoperative neck computed tomography findings revealed laryngotracheal stenosis (LTS). Conclusions Anesthesiologists should be aware that LTS may be asymptomatic and consider difficult airway guidelines in patients with history of prolonged endotracheal intubation.

Tomato fruits are consumed worldwide owing to their health benefits, taste, and flavor. In tomato cultivation, seed viability is directly related to crop productivity. Currently, the methods used to evaluate seed viability involve destructive sampling tests; accordingly, nondestructive methods for predicting seed viability are urgently required. This study aimed to develop X-ray imagery-based models capable of predicting the viability of tomato seeds. Particularly, X-ray-imaged seeds were grown to the seedling stage, and seedlings were classified following their condition. The structural integrity of the seeds was calculated from the X-ray image processing, and an integrity-based viability prediction model was evaluated. Furthermore, convolutional neural network (CNN)-based viability prediction models were developed and evaluated. Both models showed strong performance in distinguishing germinated and non-germinated seeds. However, the CNN-based model revealed greater accuracy in seed viability prediction than the image-processing-based model. The CNN-based model accuracy was 86.01%, with an F1 score of 92.11%, indicating the usefulness of the developed nondestructive testing approach for evaluating tomato seed viability.

Abstract A novel detachment method is presented for flexible substrates from glass carrier wafers using a CO2 point laser. In a conventional laser lift‐off process, laser‐induced illumination and thermal stress degrade device performance. In this approach, a point laser is applied on the edge of the flexible substrate, where electrical components do not exist, and thus, no performance degradation occurs during the laser process. In addition, the carrier surface properties are modified to control the adhesion force between the flexible substrate and the glass carrier. A spin‐on‐dielectric layer (SOD) is utilized, and Ar ion bombardment is performed on SOD. The point laser detachment method is demonstrated in flexible thin‐film transistors, and no change is observed in the electrical characteristics before and after detachment from the carrier wafer.

Abstract This work presents an analog neuromorphic synapse device consisting of two oxide semiconductor transistors for high‐precision neural networks. One of the two transistors controls the synaptic weight by charging or discharging the storage node, which leads to a conductance change in the other transistor. The programmed weight maintains for more than 300 s as electrons in the storage node are well preserved due to the extremely low off current of the oxide transistor. Ideal synaptic behaviors are achieved by utilizing superior properties of oxide transistors such as a high on/off ratio, low off current, and large‐area uniformity. To further improve the synaptic performance, self‐assembled monolayer treatment is applied for reducing the transistor conductance. The reduction of on current reduces the power consumption, and the reduced off current improves the retention characteristics. There is no noticeable decrease in simulated neural network accuracy even when the measured device‐to‐device variation is intentionally increased by 200%, indicating the possibility of large‐array operation with the synapse device.

As 5G telecom services evolve rapidly across a broad technological environment, network security in 5G landscape emerges as a critically challenging issue. One of typical network security tools is an intrusion prevention system (IPS) that monitors a network for malicious activity across the cyber-attack chain and takes action to prevent it. Vulnerabilities in 5G core networks become more varied and protocols become increasingly complex, whereby conventional Next Generation Firewall (NGFW) is not enough anymore to respond to cyber attacks. As a typical 5G vulnerability attack, PFCP-in-GTP and IPSec disable attack are highly complex to detect and cannot identify attackers without integrated session management. However, the 5G core network uses various protocols such as Non-Access Stratum (NAS), Hyper Text Transfer Protocol (HTTP), Packet Forwarding Control Protocol (PFCP), and GPRS Tunnelling Protocol (GTP), and packets of the interface used by each protocol are managed as identities that are difficult to identify. Analyzing the relationship of these interfaces in real time is an important key to integrated session management. In addition, unlike existing 4G, as 3rd Generation Partnership Project (3GPP) specs mandate encrypting 5G Standalone (SA) user IDs, it is much more difficult to identify from which user traffic has occurred in IPSs exclusive for cellular network. With regard to the above subject, this paper introduces an efficient session management scheme for users not affordable in conventional NFGW but necessarily useful for security systems in 5G SA. Furthermore, this study compared performances between conventional NGFWs and a 5G IPS system with the scheme employed, to ascertain that the scheme is feasibly implementable in 5G SA network. The actual test results show a detection rate of 99.7% and reasonable resource overhead (Memory usage 37.8%, CPU usage 42–44%).

As environmental pollution and the global population increase, and the COVID-19 pandemic becomes more severe, demands for indoor farming, especially home food gardening, have also increased. However, most research thus far has focused on large-scale food production, with very few studies having been conducted at the household scale. Also, the devices cultivating household crops with control systems in a continuous way, which minimize fluctuations of environmental conditions, have been rarely developed. Therefore, this study aimed to design a household cultivation system for sweet basil that is automatically and continuously controlled by fuzzy logic with a Raspberry Pi4. Three inputs (temperature, humidity, and growth stage) and seven outputs (fan, humidifier, heater 1, heater 2, LED red, green, and blue) were used with six rules, ensuring that three lights operated independently upon three growth stages. Simulation and actual operation were carried out, resulting in an appropriately controlled system that operated with few defects. In the case of an operation of the input variable, temperature and humidity were maintained at an average of 21.24 °C and 75.58%, respectively, and the LED operation for the growth stage was confirmed to be flawless. For verification of the designed fuzzy system, a comparison between the simulation and actual operation was performed to examine differences and identify problems. To this end, Pearson’s correlation coefficients were used, and the direction of correction of the fuzzy logic system was proposed. Through these results, the feasibility of a home cultivation system using fuzzy logic was demonstrated, and it is expected that further studies applying it will be conducted in the future.

Monodisperse colloidal InAs quantum dots have been envisioned as Pb-free materials for various infrared applications. Here, the authors provide a growth model based on monomer diffusion dynamics, enabling extended spectral coverage of InAs quantum dots beyond 1S max of 1600 nm.

Background: Perioperative myocardial infarction (PMI) is a life-threatening complication in major non-cardiac surgeries (NCS) and constitutes the most common cause of postoperative morbidity and mortality. A PMI that is associated with prolonged oxygen supply–demand imbalance and its etiology is defined as a type 2 MI. Asymptomatic myocardial ischemia can occur in patients with stable coronary artery disease (CAD), especially those with comorbidities such as diabetes mellitus (DM), hypertension, or, in some cases, without any risk factors. Case: We report a case of asymptomatic PMI in a 76-year-old patient with underlying hypertension and DM without a previous history of CAD. During the induction of anesthesia, abnormal electrocardiography was discovered, and the surgery was postponed after further studies revealed almost completely occluded three-vessel CAD and type 2 PMI. Conclusions: Anesthesiologists should closely monitor and evaluate the associated cardiovascular risk, including cardiac biomarkers of each patient before surgery, to minimize the possibility of PMI.

The demand for safe and edible meat has led to the advancement of freeze-storage techniques, but falsely labeled thawed meat remains an issue. Many methods have been proposed for this purpose, but they all destroy the sample and can only be performed in the laboratory by skilled personnel. In this study, hyperspectral image data were used to construct a machine learning (ML) model to discriminate between freshly refrigerated, long-term refrigerated, and thawed beef meat samples. With four pre-processing methods, a total of five datasets were prepared to construct an ML model. The PLS-DA and SVM techniques were used to construct the models, and the performance was highest for the SVM model applying scatter correction and the RBF kernel function. These results suggest that it is possible to construct a prediction model to distinguish between fresh and non-fresh meat using the spectra obtained by purifying hyperspectral image data cubes, which can be a rapid and non-invasive method for routine analyses of the meat storage state.

Research to deal with distributed denial of service (DDoS) attacks was kicked off from long ago and has seen technological advancement along with an extensive 5G footprint. Prior studies, and still newer ones, in the realm of DDoS attacks in the 5G environment appear to be focused primarily on radio access network (RAN) and voice service network, meaning that there is no attempt to mitigate DDoS attacks targeted on core networks (CN) by applying artificial intelligence (AI) in modeling. In particular, such components of a CN as the Access and Mobility Management Function (AMF), Session Management Function (SMF), and User Plane Function (UPF), all being principal functions enabled to provide 5G services as base stations do, provide expansive connectivity with geographically very large area coverage that cannot be matched by the base stations. Moreover, to complete re-registration for one UE, required messages in protocols Packet Forwarding Control Protocol (PFCP) and HTTP/2 are approximately 40 in number. This implies that a DDoS attack targeting the CN has, once accomplished, a greater than expected impact, when compared to DDoS attacks targeting the RAN. Therefore, security mechanisms for the CN must be put into practice. This research proposes a method, along with a threat detection system, to mitigate signaling DDoS attacks targeted on 5G SA (standalone) CNs. It is verified that the use of fundamental ML classifiers together with preprocessing with entropy-based analysis (EBA) and statistics-based analysis (SBA) enables us to proactively react against signaling DDoS attacks. Additionally, the evaluation results manifest that the random forest achieves the best detection performance, with an average accuracy of 98.7%.

The net energy requirement for lactation (NEL) equals the milk energy, which is the sum of the energy content from the energy-yielding nutrients in milk. The specific nutrients and their calories, however, vary depending on the feeding system. The objective of this study was to evaluate NEL prediction equations used in cattle feeding systems. A total of 11 equations from 6 feeding systems were assessed. For evaluation, a database was constructed based on the literature, and data for three nutrients (lactose, fat, and protein) were used to evaluate the equations. The equations were classified into three tiers based on the variables: Tier 1 (all three nutrients), Tier 2 (fat and protein), and Tier 3 (fat). NEL predicted by the equations were comparatively evaluated based on a reference value computed using Tyrrell and Reid’s equation. All equations showed high predictivity (in order, Tier 1, 2, and 3). Tier 1 equations showed a nearly perfect fit; however, for accurately predicting NEL, at least Tier 2 equations are recommended. The predictivity of theoretically derived equations was as high, or higher, as the predictivity of empirical equations. Thus, empirical development of an accurate equation to predict NEL, which requires a large amount of data, can be avoided.

Advances in mobile communication networks from 2G to 5G have brought unprecedented traffic growth, and 5G mobile communication networks are expected to be used in a variety of industries based on innovative technologies, fast not only in terms of extremely low latency but massive access devices. Various types of services, such as enhanced mobile broadband (eMBB), massive machine type communication (mMTC), and ultra-reliable and low latency communication (uRLLC), represent an increase in the number of attacks on users’ personal information, confidential information, and privacy information. Therefore, security assessments are essential to verify and cope with these various attacks. In this research, we (1) looked at 5G mobile communication network backgrounds and problems to investigate existing vulnerabilities and (2) assessed the current situation through evaluation of 5G security threats in real-world mobile networks in service.

A colorimetric sensor array was developed to characterize and quantify the taste of white wines. A charge-coupled device (CCD) camera captured images of the sensor array from 23 different white wine samples, and the change in the R, G, B color components from the control were analyzed by principal component analysis. Additionally, high performance liquid chromatography (HPLC) was used to analyze the chemical components of each wine sample responsible for its taste. A two-dimensional score plot was created with 23 data points. It revealed clusters created from the same type of grape, and trends of sweetness, sourness, and astringency were mapped. An artificial neural network model was developed to predict the degree of sweetness, sourness, and astringency of the white wines. The coefficients of determination (R2) for the HPLC results and the sweetness, sourness, and astringency were 0.96, 0.95, and 0.83, respectively. This research could provide a simple and low-cost but sensitive taste prediction system, and, by helping consumer selection, will be able to have a positive effect on the wine industry.

Purpose: This study aimed to develop and assess the feasibility and effectiveness of digital therapeutics for supportive care after gastrectomy. Materials and Method: The study included 39 patients with gastric cancer who underwent minimally invasive gastrectomy and were able to use a mobile application (app) on their smartphones. The developed research app automatically calculates and provides daily targets for calorie and protein intake based on the patient’s body mass index (BMI). Patients recorded their daily diets, weights, and symptoms in the app and completed special questionnaires to assess the feasibility of the app in real-world clinical practice. Results: At the 10-week follow-up, the mean questionnaire scores for ease of learning, usability, and effectiveness of the app (primary endpoint) were 2.32±0.41, 2.35±0.43, and 2.4±0.39 (range: 0–3), respectively. Patients were classified as underweight (<18.5, n=4), normal (18.5–24.9, n=24), or overweight (≥25.0, n=11) according to predischarge BMI. Underweight patients showed higher compliance with app usage and a higher rate of achieving the target calorie and protein intake than normal weight and overweight patients (98% vs. 77% vs. 81%, p=0.0313; 102% vs. 75% vs. 61%, P=0.0111; 106% vs. 79% vs. 64%, P=0.0429). Two patients transitioned from underweight to normal weight (50.0%), one patient (4.3%) transitioned from normal weight to underweight, and two patients (22.2%) transitioned from overweight to normal weight. Conclusions: The mobile app is feasible and useful for postoperative supportive care in terms of ease of learning, usability, and effectiveness. Digital therapeutics may be an effective way to provide supportive care for postgastrectomy patients, particularly in terms of nutrition. [ABSTRACT FROM AUTHOR]