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Retinitis pigmentosa (RP) encompasses a diverse range of hereditary, degenerative retinal ailments, presenting notable obstacles to molecular genetic diagnoses due to the intricate array of variants in different genes involved. This study enrolled 21 probands and their families who have been diagnosed with nonsyndromic RP but without a previous molecular diagnosis. We employed whole-exome sequencing (WES) to detect possible harmful gene variations in individuals with unknown-cause RP at the molecular level. WES allowed the identification of ten potential disease-causing variants in eight different genes. In 8 out of the total 21 patients, this method successfully identified the underlying molecular causes, such as putative pathogenic variants in genes including CRB1, KLHL7, PDE6B, RDH12, RP1, RPE65, USH2A, and RHO. A novel variant was identified in one of these genes, specifically PDE6B, providing valuable information on prospective targets for future enhanced gene therapeutic approaches.

Abstract We present a calculation of pseudoscalar Higgs production in association with a jet at Next-to-Next-to Leading Order (NNLO) accuracy in QCD. We work in an effective field theory in which m t → ∞ resulting in effective operators which couple the pseudoscalar to gluons and (massless) quarks. We have calculated all of the relevant amplitudes for the two-loop, one-loop and tree-level contributions. As a cross-check of our calculation we have re-calculated all of the scalar Higgs plus parton amplitudes and perform a detailed comparison to the literature. In order to regulate the infra-red singularities present at this order we employ the N-jettiness slicing method. In addition to a detailed validation of our calculation at this order we investigate LHC phenomenology for a selection of pseudoscalar Higgs masses. Our results are implemented into the parton-level Monte Carlo code MCFM.

Background: The 1996 Goseong forest fire, the largest recorded, prompted a debate on the potential for natural forest recovery, leading to the designation of a natural restoration research area. This study mainly aimed to demonstrate the forest's natural regenerative ability in a prefire Pinus densiflora forest that had been affected by a stand-replacing fire. To achieve this, the study tracked the key aspects of the succession process, specifically the formation of vertical structure and changes in species composition. Results: The regenerating vegetation initially passed through stages dominated by herbaceous and shrub layers, then differentiated into the canopy layer, eventually forming the early-stage forest after 20 years. Site A had developed into a forest with an average canopy height of 13.3 m and 73% coverage, while Site B was restored with a canopy height of 10 m and 27% coverage. Tree species of the genus Quercus dominated the canopy layer, occupying 99% of the tree basal area (from 17% prefire). Consequently, the prefire pine forest shifted to a Quercus-dominated forest after secondary succession. Pinus densiflora (83% of the prefire basal area) occupied only 1% after 20 years. Oak species became dominant from the initial stages of regeneration, playing a key role in shaping the early-stage forest structure. The species composition of the regenerating stands was already determined in the initial stage and closely resembled that of 20 years later. Since most species regenerated through resprouting, the understory remained dominated by pine forest companion species. Oak-associated species tended to increase in later stages. No invasive species were observed, and annual plants had low abundance. Conclusions: The study demonstrated the natural regenerative power of the forest following the fire, revealing that it takes around 20 years for a prefire pine forest to be restored an early-stage oak-dominated forest. Quercus trees, particularly Quercus variabilis and Quercus mongolica, regenerated rapidly immediately after fire, contributing to the development of the early-stage forest. Although this study was small in scale, it is a rare study conducted at permanent plots over 20 years, revealing the secondary succession process.

In this study, the form removal time of concrete was evaluated using ultrasonic pulse velocity (UPV). The evaluation was performed based on the form removal strength specified in the American Concrete Institute (ACI), British Standard (BS), and Korean Construction Standards (KCS). Additionally, physical and mechanical properties of the concrete were evaluated from the early stages up to 28 days after casting. Specimens of both plain and lightweight concrete (LWC) were prepared for analysis. For the purpose of evaluating the concrete at various strengths, the water-to-cement (W/C) ratios were established at 0.41 and 0.28, aimed at achieving target compressive strengths of 30 MPa and 60 MPa for plain concrete, respectively. The measurement of mechanical properties was conducted through the assessment of penetration resistance, compressive strength, and ultrasonic pulse velocity (UPV). Models for predicting the strength based on regression analysis were proposed, taking into account the characteristics of the setting times (both initial and final). Additionally, the aggregate cross-section, matrix structure, and crystalline phases were examined using scanning electron microscopy (SEM) and X-ray diffraction (XRD). In the results of the experiments, it was observed that Mortar60 reached both initial and final set quicker than Mortar30, a phenomenon attributed to the higher cement content. Initially, Plain and LWC specimens exhibited comparable trends in compressive strength and ultrasonic pulse velocity (UPV). However, when examining the compressive strength development from 1 to 28 days, a notable difference of approximately 33 % was observed between Plain60 and LWC60. Similarly, differences in UPV ranged from 10.12 % to 11.88 %. The logistic regression model was found to predict the form removal strength more accurately than the exponential model, as indicated by the regression analysis results. When these results were compared with previous strength prediction models, the difficulty in predicting form removal strength became evident, primarily due to the substantial errors present in these models. Analysis using scanning electron microscopy (SEM) and X-ray diffraction (XRD) revealed the presence of hydration products within the lightweight aggregate, which signifies the hydration reaction. These products seemed to enhance the performance of the interfacial transition zone (ITZ) between the aggregate and cement paste. Furthermore, Mortar60 was characterized by larger crystalline hydration products, a denser matrix structure, and higher XRD peaks in comparison to Mortar30.

IntroductionThe effectiveness and safety of acupuncture in the treatment of obesity have not been assessed. This poses a challenge for clinicians who choose to use acupuncture in the treatment of obesity, as they are unable to prioritize this approach based on outcome variables.MethodsIn May 2024, a literature search of five databases was conducted. Only randomized controlled trials evaluating body weight (BW), body mass index, waist circumference (WC), and adverse events in patients with a body mass index (BMI) of 25 or higher for various acupuncture modalities were included. The risk of bias was assessed using the Cochrane risk-of-bias tool for randomized trials, version 2. Pairwise meta-analysis (PMA) and Bayesian network meta-analysis (NMA) were performed using a random effects model for quantitative synthesis.ResultsFourteen studies (n = 868) were included. The included studies evaluated the following acupuncture modalities: electroacupuncture (EA) (N = 6), laser acupuncture (LA) (N = 2), auricular acupuncture (AA) (N = 5), and manual acupuncture (MA) (N = 3). The PMA found that adding EA to usual care (UC), compared to UC alone, reduced BW (MD = 2.46, 95% CI = 1.12 to 3.80, I2 = 58%, REM, N = 3, n = 157). The NMA of BW showed the following effect sizes for UC alone versus each acupuncture modality combined with UC: LA (MD = 2.09, 95% CI = 0.04 to 3.86), EA (MD = 2.04, 95% CI = 0.88 to 3.50), AA (MD = 1.69, 95% CI = −0.11 to 3.58), and MA (MD = 1.02, 95% CI = −0.82 to 2.94). The probability of each modality being the optimal treatment was evaluated using the surface under the cumulative ranking curve. EA was the most efficacious for BW and BMI, while LA was the most efficacious for WC.DiscussionEA and LA can effectively complement clinical obesity management. The number of included studies was limited, and publication bias may have occurred, necessitating a cautious interpretation of the results. Furthermore, most studies lasted between six and 12 weeks. Future clinical studies of acupuncture for obesity should include longer follow-up periods.Systematic review registrationhttps://www.crd.york.ac.uk/prospero/display_record.php?RecordID=387788, identifier CRD42023387788.

Objective A preliminary report from a single institution, noninferiority, prospective randomized controlled trial is conducted to determine the effectiveness of interlaminar endoscopic lumbar discectomy (IELD) versus microscopic lumbar discectomy (MLD) for the treatment of L5–S1 lumbar disc herniation (LDH). Methods This prospective, noncrossover, randomized controlled trials was conducted at a single neurosurgical center. Patients with symptomatic radiculopathy or intermittent neurogenic claudication caused by LDH were enrolled from July 2016 to July 2021. The study compared the effectiveness of microscopic and full-endoscopic discectomy procedures. Outcome measures included visual analogue scale (VAS) scores for back and leg pain, Oswestry Disability Index scores, radiologic measurements, endurance time of walking, and satisfaction rate. Results Of 37 assessed patients, both IELD and MLD groups demonstrated significant improvements in VAS scores for pain over time, with no significant difference between them. For secondary outcomes, the IELD group had a shorter hospital stay and reduced blood loss but a longer operation time than the MLD group. Radiographic evaluations showed no change compared to preoperative data. Patient satisfaction and recovery rates were slightly higher for the MLD group, but both groups were comparable in most evaluations, with complications being minimal. Conclusion The IELD was noninferior in improving the intensity of back and leg pain and functional disability, compared to the MLD. Additionally, the IELD showed no difference in clinical outcomes for patients in terms of radiographic results and patient satisfaction rates. The results of this research preliminarily demonstrate that the IELD could be considered an effective alternative to MLD for L5–S1 central or paracentral LDH.

Aramid copolymers have garnered significant interest due to their potential applications in extreme environments such as the aerospace, defense, and automotive industries. Recent developments in aramid copolymers have moved beyond their traditional use in high-strength, high-temperature resistant fibers. There is now a demand for new polymers that can easily be processed into thin films for applications such as electrical insulation films and membranes, utilizing the inherent properties of aramid copolymers. In this work, we demonstrate two novel aramid copolymers that are capable of polymerizing in polar organic solvents with a high degree of polymerization, achieved by incorporating flexible bis(4-aminophenoxy) benzene moieties into the chain backbone. The synthesized MBAB-aramid and PBAB-aramid have enabled the fabrication of exceptionally thin, clear films, with an average molecular weight exceeding 150 kDa and a thickness ranging from 3 to 10 μm. The dynamic mechanical analysis (DMA) and thermogravimetric analysis (TGA) reveal that the thin films of MBAB-aramid and PBAB-aramid exhibited glass transition temperatures of 270.1 °C and 292.7 °C, respectively, and thermal decomposition temperatures of 449.6 °C and 465.5 °C, respectively. The mechanical tensile analysis of the 5 μm thick films confirmed that the tensile strengths, with elongation at break, are 107.1 MPa (50.7%) for MBAB-aramid and 113.5 MPa (58.4%) for PBAB-aramid, respectively. The thermal and mechanical properties consistently differ between the two polymers, which is attributed to variations in the linearity of the polymer structures and the resulting differences in the density of intermolecular hydrogen bonding and pi-pi interactions. The resulting high-strength, ultra-thin aramid materials offer numerous potential applications in thin films, membranes, and functional coatings across various industries.

Onion (Allium cepa) is a major field vegetable in South Korea and has been produced for a long time along with cabbage, radish, garlic, and dried peppers. However, as field vegetables, including onions, have recently been imported at low prices, the profitability of onion production in South Korea is beginning to be at risk. In order to maximize farmers’ profits through onion production, this study develops onion yield prediction models via an ensemble learning-based framework involving linear regression, polynomial regression, support vector regression, decision tree, ridge regression, and lasso regression. The use of nitrogen fertilizers is considered an independent variable in the development of the yield prediction model. This is because the use of nitrogen fertilizers accounts for the highest production cost (13.47%) after labor cost (41.21%) and seed cost (17.42%), and it also directly affects onions yields. For the model development, five research datasets on changes in onion yield according to changes in the use of existing nitrogen fertilizers were used. In addition, a non-linear optimization model was devised using onion yield prediction models for the profit maximization of onion production. As a result, the developed non-linear optimization model using polynomial regression enables an increase in profits from onion production by 67.28%.

Hydrogen has great growth potential due to its green, carbon-neutral nature, but public acceptance is low due to negative perceptions of the dangers associated with hydrogen energy. Safety concerns, particularly related to its flammability and explosiveness, are an obstacle to hydrogen energy policy. In South Korea, recent hydrogen-related explosions have exacerbated these concerns, undermining public confidence. This study developed public relations (PR) strategies to manage risk perception and promote hydrogen energy acceptance by analyzing the opinions of government officials and experts using SWOT factors, the TOWS matrix, and the analytic hierarchy process. The findings highlight the importance of addressing weaknesses and threats in PR efforts. Key weaknesses include Korea’s technological lag and the low localization of core hydrogen technologies, both of which hinder competitiveness and negatively impact public perception of hydrogen energy. Notable threats include deteriorating energy dependency and expanding global carbon regulations. This information can be used to influence attitudes and foster public acceptance of hydrogen energy policies. Emphasizing weaknesses and threats may result in more effective PR strategies, even if they do not directly address the primary concerns of scientific experts. The persuasive insights identified in this study can support future policy communication and PR strategies.

Abstract Natural, organic, materials-based artificial synaptic devices have been in the spotlight for wearable/flexible devices due to their lightweight, biocompatibility, and scalability. In this study, an electronic memristive device based on agarose extracted from plants in the Rhodophyceae class was fabricated, and its memory characteristics and analog data processing capabilities were evaluated. The Al/agarose@gold nanoparticle (AuNP) film/indium-tin-oxide (ITO)-structured memristive device exhibited reliable resistive switching characteristics with excellent retention with a large Ron/Roff ratio of 104. Also, analog conductance changes in our device were achieved with power consumption at the pJ level. This notable behavior could be maintained under mechanical deformations from a flat to a 4-mm bent state. In the recognition simulation based on the device's performance, an 91% accuracy and clear digit classification were achieved.

Indoor fires may cause casualties and property damage, so it is important to develop a system that predicts fires in advance. There have been studies to predict potential fires using sensor values, and they mostly exploited machine learning models or recurrent neural networks. In this paper, we propose a stack of Transformer encoders for fire prediction using multiple sensors. Our model takes the time-series values collected from the sensors as input, and predicts the potential fire based on the sequential patterns underlying the time-series data. We compared our model with traditional machine learning models and recurrent neural networks on two datasets. For a simple dataset, we found that the machine learning models are better than ours, whereas our model gave better performance for a complex dataset. This implies that our model has a greater potential for real-world applications that probably have complex patterns and scenarios.

This paper presents a 2.4/5 GHz dual-band low-noise and highly linear receiver (RX) for IEEE 802.11a/b/g/n/ac/ax applications. The RX employs an RF front-end architecture composing a balun-low-noise transconductance amplifier, current-mode passive mixers, and transimpedance amplifiers (TIAs) to achieve compactness, low noise, and high linearity. A novel power-efficient feedforward operational amplifier architecture was also implemented in the TIA design to achieve the target RX error vector magnitude (EVM) of less than −35 dB with modulation and coding scheme 11 and 1024 quadrature amplitude modulation. Fabricated in a 22-nm SOI CMOS process, the RX achieved noise figures of 2.1/4.2 dB, in- band input-referred third-order intercept points of −11.8/−7.5 dBm, EVMs of −46.4/−44.6 dB for the 2.4/5 GHz bands, respectively. It drew a bias current of 21 mA from a nominal supply voltage of 0.8 V. The active die area was 0.65 mm2.

The development of a metasurface lens, which represents a flat optical element with subwavelength thickness, holds the potential for miniaturizing imaging systems. Nevertheless, the presence of aberrations poses a significant challenge for achieving optimal imaging quality out of it. Moreover, its practical utilization in cameras and wearable devices necessitates simultaneous correction of monochromatic and chromatic aberrations. We here propose a framework to design multi-layered, dispersion-engineered metasurfaces, which is capable of correcting monochromatic and chromatic aberrations in a simultaneous manner. Based on the scheme, we numerically demonstrate a novel metasurface doublet that gives rise to significant improvement in correction of both monochromatic and chromatic aberrations. The designed lens has a field-of-view of 50° and operates in the broadband visible region from 450 to 650 nm.

Thyroid hormones are known to influence the production and secretion of pulmonary surfactant. The objective of this study was to explore the relationship between respiratory distress syndrome (RDS) and thyroid hormones. This was a retrospective study of preterm infants at 24–33 weeks gestational age from April 2017 to February 2019. T3, free T4 (fT4), and thyroid-stimulating hormone (TSH) were measured 1, 3, and 6 weeks after birth. Multivariate logistic regression analyses were performed to determine the relationship between RDS and TSH. A total of 146 infants were enrolled. Of these, 60 had RDS, 72 had no RDS, and 14 were excluded. T3 and TSH were lower in the RDS groups (p < 0.05) on the day of birth. Multivariate logistic regression analysis indicated that lower serum TSH levels immediately after birth were associated with a higher incidence of RDS (OR, 0.89; 95% CI, 0.81–0.97). The TSH level was associated with the incidence of RDS. This suggests that suppression of the hypothalamus–pituitary axis function contributes to RDS, which is the result of surfactant deficiency.

The efficacy and safety of endovascular treatment (EVT) for moyamoya disease (MMD) have rarely been investigated. The objective of this study was to summarize the clinical outcomes of EVT for MMD and determine the potential role of EVT in treating symptomatic steno-occlusive lesions in MMD. Reports from January 2000 to December 2021 describing EVT in MMD were collected through a literature search. The search terms included “moyamoya”, “stent”, “angioplasty”, and “endovascular”. Data regarding baseline demographics, previous medical history, treated vessel, periprocedural complications, and angiographical recurrence were retrieved. This review included 10 studies with details of 19 patients undergoing a total of 31 EVT procedures. Twenty-one EVTs were performed as initial treatments for MMD, and 10 were performed as additional treatments for angiographical recurrence. The mean follow-up period of the initial EVTs was 9.0±11.9 months, with angiographical recurrence in 11 (68.8%) cases. The mean follow-up period of additional EVTs was 4.3±3.9 months, and seven (70.0%) EVTs showed restenosis of the re-treated vessel. Across all initial and additional EVTs, there were no differences in characteristics between the recurrence and non-recurrence groups. Overall, two periprocedural complications (9.5%) occurred, one vessel rupture and one massive intracerebral hemorrhage with subarachnoid hemorrhage. EVT plays a limited role in the management of symptomatic intracranial arterial steno-occlusive lesions of MMD. Recent advances in understanding the pathomechanism of MMD may urge neuro-interventionists to find a new endovascular approach with better balloon angioplasty or stenting mechanisms.

Abstract Unsafe electrical appliances can be hazardous to humans and can cause electrical fires if not monitored, analyzed, and controlled. The purpose of this study is to monitor the system’s condition, including the electrical properties of the appliances, and to diagnose fault conditions without deploying sensors on individual appliances and analyzing individual sensor data. Using historical data and an acceptable range of normal and leakage currents, we proposed a hybrid model based on multiclass support vector machines (MSVM) integrated with a rule-based classifier (RBC) to determine the changes in leakage currents caused by installed devices at a certain moment. For this, we developed a sensor-based monitoring device with long-range communication to store real-time data in a cloud database. In the modeling process, RBC algorithm is used to diagnose the constructed device fault and overcurrent fault where MSVM is applied for detecting leakage current fault. To conduct an operational field test, the developed device was integrated into some houses. The results demonstrate the effectiveness of the proposed system in terms of electrical safety monitoring and detection. All the collected data were stored in a structured database that could be remotely accessed through the Internet.

Sciatic notch tumours of the intra-pelvic variety are rare lesions and commonly arise from the sciatic nerve. These are usually benign neurogenic tumours and the most common of them is schwannoma. Conventional laparotomy techniques have associated surgical morbidity and significant blood loss. The advent of robotic systems and refinement in robotic-assisted surgical techniques to access deep lying pre-sacral lesions has offered a novel way to surgically handle such lesions thereby reducing morbidity, hospital stay and blood loss. Here we are presenting a case of non-discogenic sciatica which was attributed to superior gluteal nerve tumour and was surgically resected using robotic-assisted technique.

Artificial graphite (AG), in contrast to natural graphite, has received much attention due to its superior rate capabilities and stability under high current density. Although the rate capabilities can be enhanced by employing artificial graphite, there is room for improvement in the cycle retention characteristics of artificial graphite. In particular, further optimization on the binder type on anodes is necessary, as commercially used polyvinylidene fluoride (PVdF) has limitations in long-term cycling stability. Furthermore, PVdF is dissolved in N-Methyl-2-pyrrolidone (NMP), which is not environmentally- and eco-friendly. In this work, we have introduced a water-soluble binder based on deacetylated kondagogu gum (KG) for AG anodes, which are derived from the gum tree. Upon deacetylation, kondagogu gum can be dissolved in water, which can be directly used together with active materials and conductive agents in a slurry. Replacing a PVdF binder with a KG binder results in an enhanced cycle retention and comparable rate capabilities, without the changes in the overall redox reactions with Li+. Postmortem analysis reveals that the improved electrochemical performance of the AG anode contributed to the excellent structural integrity of the KG binder compared with the PVdF binder.

Bread is one of the most consumed foods in the world, and modern food processing technologies using artificial intelligence are crucial in providing quality control and optimization of food products. An integrated solution of sensor data and machine learning technology was determined to be appropriate for identifying real-time changing environmental variables and various influences in the baking process. In this study, the Baking Process Prediction Model (BPPM) created by data-based machine learning showed excellent performance in monitoring and analyzing real-time sensor and vision data in the baking process to predict the baking stages by itself. It also has the advantage of improving the quality of bread. The volumes of bread made using BPPM were 127.54 ± 2.54, 413.49 ± 2.59, 679.96 ± 1.90, 875.79 ± 2.46, and 1260.70 ± 3.13, respectively, which were relatively larger than those made with fixed baking time (p < 0.05). The developed system is evaluated to have great potential to improve precision and efficiency in the food production and processing industry. This study is expected to lay the foundation for the future development of artificial intelligence and the food industry.

Cloud gaming services enable users to enjoy various high-quality computer games without installing them in their devices which may have poor resources. However, it consumes high bandwidth in backbone networks as game videos have to be constantly streamed to client devices, which can be a hindering factor for providing Ultra High Definition (UHD)-quality cloud gaming services whose demands are ever increasing. In this paper, we present a novel cloud gaming architecture, E-Render, in which the graphics engine is separated from the cloud game server and is deployed in the edge for rendering. To realize it, however, we need to effectively reduce the amount and latency of graphics commands transfer from the game engine in the cloud to the graphics engine in the edge server. E-render solves the problem by using novel techniques including multi-granular command deduplication and asynchronous buffer binding. We implement a prototype cloud gaming platform based on the E-Render architecture, and evaluate its performance using four real world games. The evaluation results confirm that E-Render can provide UHD-quality cloud gaming services.

Abstract If the speed of machine learning is to be improved, devices and systems with strong resistances to various types of internal noise, mainly internal thermal noise, are urgently needed. The successful demonstration of a synaptic device is reported based on a polyimide–tungsten disulfide quantum dot (PI‐WS2 QD) nanocomposite that continued to operate normally after simultaneous exposure of a high temperature of 100 °C. Such excellent performance is attributable to the strong quantum confinement effect of WS2 QDs. The working current of the device and its power consumption are on the orders of nanoamperes and femtojoules, respectively. Undoubtedly, such devices will significantly improve the physical performances of machine learning systems and allow the rapid realization of greatly improving machine learning speed.

This study evaluated the physicochemical characteristics and storage stability (at 0, 3, and 7 days) of hybrid beef patties with different amount of shiitake mushrooms (Lentinus edodes) added. Shiitake mushrooms contain healthy ingredients such as ergosterol and β-glucan. Four proportions of shiitake mushrooms were added to beef patties (T1, 20%, T2, 40%, T3, 60%, T4, 80%) as a substitute for beef and compared with a control group (CON 0%). Chemical composition, water holding capacity (WHC), cooking loss, pH, color, texture profile analysis, and sensory properties of the products were compared on day 0. As a storage stability experiment, volatile basic nitrogen (VBN), 2-thiobarbituric acid reactive substances (TBARS), and total microbial count were compared (0, 3, and 7 days). The results revealed that replacement with shiitake improved the WHC and cooking loss of patties but had a negative effect on sensory properties and storage stability. These results indicate that shiitake mushrooms can be added along with beef to produce hybrid patties; however, the usage amount must be considered.

Smart farming solutions combine information, data software tools, and technology with the intent to improve agricultural production. While smart farming concepts are well described in the literature, the potential societal impacts of smart farming are less conspicuous. To demonstrate how smart farming solutions could influence future agricultural production, agri-business and rural communities and their constituents, this article compares smart farming approaches and reasons behind the pursuit of smart farming solutions by the U.S. and South Korea. The article compares agricultural assets and productivity among the two countries as well as the technical and societal challenges impacting agricultural production as a basis to understanding the motivations behind and pathways for developing smart farming solutions. In doing so, the article compares some of the technological and social advantages and disadvantages of smart farming, dependending on the choice and implementation of smart farming solutions. The South Korean government has implemented a national policy to establish smart farming communities; a concept that addresses the entire agri-food supply chain. In the U.S., a national plan to develop smart farming technologies does not exist. However, discrete smart farming solutions driven mainly by competition in the private sector have resulted in high-tech solutions that are advancing smart farming concepts. The differences in approaches and reporting of successes and failures between the two countries could facilitate the rate of evolution of successful smart farming solutions, and moreover, could provide pathways to facilitate sustainable development goals in developing countries where smart farming activities are currently underway.

Abstract Artificial synaptic devices based on natural organic materials are becoming the most desirable for extending their fields of applications to include wearable and implantable devices due to their biocompatibility, flexibility, lightweight, and scalability. Herein, we proposed a zein material, extracted from natural maize, as an active layer in an artificial synapse. The synaptic device exhibited notable digital-data storage and analog data processing capabilities. Remarkably, the zein-based synaptic device achieved recognition accuracy of up to 87% and exhibited clear digit-classification results on the learning and inference test. Moreover, the recognition accuracy of the zein-based artificial synapse was maintained within a difference of less than 2%, regardless of mechanically stressed conditions. We believe that this work will be an important asset toward the realization of wearable and implantable devices utilizing artificial synapses.

Microbial fuel cell (MFC) technology is receiving a lot of attention recently as a promising technology for generating electricity by treating organic waste resources. Over the past 20 years, the MFC technology has made rapid progress: various research on system architectures, electrochemistry, materials, and microbiology has been conducted for developing practical ideas and fundamental principles. Recently, a lot of research on scaled-up systems for practical application is being conducted in the MFC field. In this review, materials, electrochemistry, system development and scale-up systems of MFCs studied so far are reviewed, and future prospects and directions of MFC technology are presented.

This paper presents a wireless marine buoy system based on the seawater battery (SWB), providing self-powered operation, power-efficient management, and degradation prediction and fault detection. Since conventional open circuit voltage (OCV) methods cannot be applied due to inherent cell characteristics of SWB, the coulomb counting (CC) method is adopted for the state of charge (SOC) monitoring. For the state of health (SOH), a variance-based detection scheme is proposed to provide degradation prediction and fault detection of the SWB. The self-powered operation is augmented by two proposed power optimization schemes such as multiple power management and three-step LED light control. A wireless buoy system prototype is manufactured, and its functional feasibility is experimentally verified, where its location and SOC are periodically monitored in a smartphone-based wireless platform.

Due to the increasing market share of electric vehicles (EVs), the optimal thermal management (TM) of batteries has recently received significant attention. Optimal battery temperature control is challenging, requiring a detailed model and numerous parameters of the TM system, which includes fans, pumps, compressors, and heat exchangers. This paper proposes a supervised learning strategy for the optimal operation of the TM system in an EV. Specifically, for TM subsystems, individual artificial neural networks (ANNs) are implemented and trained with data obtained under normal EV driving conditions. The ANNs are then interconnected based on the physical configuration of the TM system. The trained ANNs are replicated using piecewise linear equations, which can be explicitly integrated into an optimization problem for optimal TM scheduling. This approach enables the application of a mixed-integer linear programming solver to the problem, ensuring the optimality of the solution. Simulation case studies are performed for the two operating modes of the TM system: i.e., integrated and separate modes. The case study results demonstrate that the ANN-based model successfully reflects the operating characteristics of the TM system, enabling accurate battery temperature estimation. The proposed optimal TM strategy using the ANN-based model is verified as effective in reducing the total energy consumption, while maintaining the battery temperature within an acceptable range.

The Internet of Things (IoT) is evolving rapidly and requires IoT devices to have more resources to meet the growing needs in diverse application domains. Despite increasing demands, modern IoT devices do not fully utilize somewhat over-provisioned computing resources. In this work, we introduce a new concept of IoT-assisted Edge Computing which makes use of consolidated idle resources in IoT devices for edge services through offloading edge tasks to nearby IoT devices. For the IoT-assisted edge computing be beneficent, two important conditions should be satisfied: 1) offloaded edge tasks to IoT devices do not hurt normal execution of local IoT tasks, and 2) computing resources in IoT devices should be effectively exploited to increase the throughput of edge services. To that end, we propose a collaborative task scheduling for IoT-assisted edge computing, in which an edge node determines where to offload edge tasks among participating IoT devices based on the offloaded execution time and energy consumption, and each IoT device determines when to execute the offloaded tasks considering local tasks execution. Experimental results show that the proposed scheme not only achieves near-optimal task throughput but also outperforms other scheduling algorithms in terms of deadline satisfaction ratio of time critical tasks, while guaranteeing deadlines of local tasks in IoT devices.

Reinforced concrete is the most commonly used material in the construction industry. However, one disadvantage of reinforced concrete is that environmental factors cause materials to penetrate the concrete and cause steel bar corrosion. Rebar corrosion increases its volume significantly by approximately 3–6 times, which lowers concrete–rebar adhesion. This severely affects the serviceability and durability of concrete structures. The economic and social impacts of such deterioration are extremely large. To reduce corrosion, glass fiber-reinforced plastics (GFRP) and carbon fiber-reinforced plastics (CFRP) can be applied to concrete. The rebar–concrete bond strength is an important factor to be considered while applying GFRP and CFRP. Thus, we experimentally investigated the adhesion strength of GFRP and CFRP in relation to the strength of concrete and water–cement ratio according to ASTM C 234 to correlate the data for the development of GFRP and CFRP as substitutes for deformed reinforcing bars. The results showed that a lower water–cement ratio yielded higher compressive strength and bond strength; the bond strength of GFRP was approximately 23% lower than that of CFRP. The coating of the rebar surface required for GFRP and CFRP application in reinforced concrete structures ought to be investigated in the future.

The demand for efficient water use and automatic systems has been increasing due to the frequent drought damage to crops as a result of climate change, the shortage of water resources in rural areas, and the aging of farmers. The existing automatic irrigation systems reduce the amount of labor required for irrigation and maintain soil moisture. However, the irrigation threshold criteria are user-determined as opposed to being automated according to input objectives such as improving crop productivity and saving water. In this study, an algorithm that could automatically determine suitable soil moisture according to a database and an automatic irrigation system with intermittent irrigation for efficient water use were developed. An experiment was then conducted on the productivity of crops for protected cultivation according to the application of the system. As the frequency domain reflectometry (FDR) sensor used in this system measured the volumetric water content of the soil, the soil moisture tension corresponding with the set value was converted into the volumetric water content using a regression equation. The process of intermittent irrigation was defined by using the moisture movement modeling of Hydrus 2D to reduce water loss on the soil surface and allow moisture to penetrate the soil unobstructed. An experimental field of a tomato farm was divided into empirical manual and controlled automatic irrigation plots. A total of 97.3% of the soil moisture values in the −33 kPa-controlled automatic irrigation plot and 96.6% of the soil moisture values in the −25 kPa-controlled automatic irrigation plot were within each set range during the first cropping season. During the second cropping season, a total of 94.8% of the soil moisture values in the −33 kPa-controlled automatic irrigation plot was within the set range. Compared with the empirical manual irrigation plot, the water productivity in the first cropping season was 113.9% in the −33 kPa-controlled automatic irrigation plot and 106.3% in the −25 kPa-controlled automatic irrigation plot. In the second cropping season, the water productivity was 117.3% in the −33 kPa-controlled automatic irrigation plot. Therefore, an automatic irrigation system applied with intermittent irrigation could be critical to increasing agricultural production and improving water-use efficiency.

We make daily comments on online platforms (e.g., social networks), and such natural language texts often contain sentiment (e.g., positive and negative) for certain aspects (e.g., food and service). If we can automatically extract the aspect-based sentiment from the texts, then it will help many services or products to overcome their limitations of particular aspects. There have been studies of aspect sentiment classification (ASC) that finds sentiment towards particular aspects. Recent studies mostly adopt deep-learning models or graph neural networks as these techniques are capable of capturing linguistic patterns that contributed to performance improvement in various natural language processing tasks. In this paper, for the ASC task, we propose a new hybrid architecture of graph convolutional network (GCN) and recurrent neural network. We design a gate mechanism that jointly models word embeddings and syntactic representation of sentences. By experimental results on five datasets, we show that the proposed model outperforms other recent models and also verify that the gate mechanism contributes to the performance improvement. The overall F1 scores that we achieved is 66.64∼76.80%.

APV systems producing both crops and electricity are becoming popular as an alternative way of producing renewable energy in many countries with land shortage issues (e.g., South Korea). This study aims at developing a hybrid performance model of an Agrophotovoltaic (APV) system that produces crops underneath the PV modules. In this study, the physical model used to estimate solar radiation is integrated with a polynomial regression approach to forecast the amount of electricity generation and crop production in the APV system. The model takes into account not only the environmental factors (i.e., daily temperature, precipitation, humidity, and wind speed) but also physical factors (i.e., shading ratio of the APV system) related to the performance of the APV system. For more accurate modelling, the proposed approach is validated based on field experiment data collected from the APV system at Jeollanam-do Agricultural Research and Extension Services in South Korea. As a result, the proposed approach can predict the electricity generation quantity in the APV system with an R2 of 80.4%. This will contribute to the distribution of the APV system, which will increase farmers’ income as well as the sustainability of our society.

The Sudanese Government launched the National SDG-6 Plan and commences its implementation to achieve and sustain universal and equitable access to basic WASH services by 2030. It is critical to understand the geographical heterogeneity of Sudan and patterns in the inequality of access to safe drinking water and sanitation. Through such research, the disease control strategy can be optimized, and resource allocation can be prioritized. We explored spatial heterogeneity and inequality in access to improved water and sanitation across Sudan by mapping the coverage at both the state and district levels. We decomposed the inequality across Sudan into within-state, between-state, within-district, and between-state inequalities using the Theil L and Theil T indices. We calculated the Gini coefficient to assess the inequality of access to improved water and sanitation, based on the deviation of the Lorenz curve from the line of perfect equality. The study population was 105,167 students aged 8–13 at 1,776 primary schools across the country. Geographical heterogeneity was prominent in the Central Darfur, South Darfur, East Darfur, Kassala, West Kordofan, and Blue Nile States, all of which showed severe inequality in access to an improved latrine at the household level in terms of the Theil T or Theil L index. The overall inequality in the coverage of improved sanitation went beyond the warning limit of 0.4 for the Gini coefficient. The inequality in terms of the Theil L and Theil T indices, as well as the Gini coefficient, was always higher for improved sanitation than for improved water at the household level. Within-state inequality accounted for 66% or more of national inequalities in the distribution of improved sanitation and drinking water for both the Theil L and Theil T indices. This is the first study to measure geographical heterogeneity and inequalities in improved water and sanitation coverage across Sudan. The study may help to prioritize resource allocation to areas with the greatest water and sanitation needs.

Analyses and comparisons of generic models and a novel modeling method of line-commutated converter (LCC)-based high voltage direct current (HVDC) systems are presented for the power system simulator for engineering (PSS/E) simulation tool. The main purposes are to describe the dynamics of the DC voltage and current of HVDC system and to improve estimation of the maximum values using the generic and proposed HVDC models when AC line-to-ground faults are occurred. For the generic HVDC models, the characteristics and limitations as well as parameter investigations are conducted. Three modules are also are developed for the proposed HVDC model, which are composed of (a) AC/DC conversion; (b) controller selection; and (c) DC line model. Case studies were conducted considering the real-operated HVDC system in Korea under the PSS/E and power systems computer aided design (PSCAD) simulation environments. The simulation results were compared with measured data from the real-operated HVDC system and the results from the PSCAD HVDC models considering single- and three-phase line-to-ground faults. The case study results show that a specific generic HVDC model in PSS/E can simulate the dynamics of the DC voltage and current after the AC line-to-ground faults. The case studies also showed that the proposed modeling method is effectively improves the estimation of the maximum DC voltage and current values for the AC line-to-ground faults.

Recently, optical dielectric metasurfaces, ultrathin optical skins with densely arranged dielectric nanoantennas, have arisen as next-generation technologies with merits for miniaturization and functional improvement of conventional optical components. In particular, dielectric metalenses capable of optical focusing and imaging have attracted enormous attention from academic and industrial communities of optics. They can offer cutting-edge lensing functions owing to arbitrary wavefront encoding, polarization tunability, high efficiency, large diffraction angle, strong dispersion, and novel ultracompact integration methods. Based on the properties, dielectric metalenses have been applied to numerous three-dimensional imaging applications including wearable augmented or virtual reality displays with depth information, and optical sensing of three-dimensional position of object and various light properties. In this paper, we introduce the properties of optical dielectric metalenses, and review the working principles and recent advances in three-dimensional imaging applications based on them. The authors envision that the dielectric metalens and metasurface technologies could make breakthroughs for a wide range of compact optical systems for three-dimensional display and sensing.

Background. Bioavailable 25-hydroxy vitamin D (25(OH)D) has been suggested for the accurate determination of vitamin D status. The purpose of this study was to determine the utility of bioavailable 25(OH)D in assessing vitamin D status when vitamin D-binding protein (VDBP) was significantly altered by pregnancy and liver cirrhosis (LC). The role of genotyping of GC, a gene encoding VDBP, in the determination of bioavailable 25(OH)D concentration in a Korean population was also evaluated. Methods. This prospective study enrolled a total of 136 subjects (53 healthy controls, 45 patients with LC, and 38 pregnant women) from 2017 to 2018. The concentrations of total 25(OH)D and VDBP were measured, and bioavailable 25(OH)D concentrations were calculated. GC genotyping was performed to determine rs4588 and rs7041 polymorphisms. Clinical and laboratory data were compared among the three groups of subjects. Results. Median VDBP and total 25(OH)D concentrations were 165.2 μg/ml and 18.5 ng/ml in healthy controls, 76.9 μg/ml and 10.5 ng/ml in patients with LC, and 368.9 μg/ml and 17.7 ng/ml in pregnant women, respectively. Compared with controls, patients diagnosed with LC had significantly lower VDBP and total 25(OH)D concentrations (all P

In this work, we fabricated quantum dot light-emitting diodes using solution-processed NiO as the hole injection layer to replace the commonly used poly(3,4-ethylenedioxythiophene): poly(styrene-sulfonate) (PEDOT:PSS) layer. We successfully prepared NiO films by spin coating the NiO precursor, then annealing them, and then treating them with UV-ozone under optimized conditions. The best device with the NiO film shows higher current efficiency (25.1 cd/A) than that with the PEDOT:PSS layer (22.3 cd/A). Moreover, the long-term stability of the devices with NiO which is annealed at 500 °C is improved substantially. These results suggest that the NiO layer can be a good alternative for developing stable devices.

An appropriate spacer design helps in minimizing membrane fouling which remains the major obstacle in forward osmosis (FO) systems. In the present study, the performance of a hole-type spacer (having holes at the filament intersections) was evaluated in a FO system and compared to a standard spacer design (without holes). The hole-type spacer exhibited slightly higher water flux and reverse solute flux (RSF) when Milli-Q water was used as feed solution and varied sodium chloride concentrations as draw solution. During shale gas produced water treatment, a severe flux decline was observed for both spacer designs due to the formation of barium sulfate scaling. SEM imaging revealed that the high shear force induced by the creation of holes led to the formation of scales on the entire membrane surface, causing a slightly higher flux decline than the standard spacer. Simultaneously, the presence of holes aided to mitigate the accumulation of foulants on spacer surface, resulting in no increase in pressure drop. Furthermore, a full cleaning efficiency was achieved by hole-type spacer attributed to the micro-jets effect induced by the holes, which aided to destroy the foulants and then sweep them away from the membrane surface.

There are so many variables affecting the large-scale chemical synthesis of nanoparticles that mass production remains a challenge. Here, using a high-efficiency compact electron beam generator irradiating a low-energy electron beam, we fabricate carbon-supported Pt nanoparticles (Pt/C) in an open chamber to present the applicability of an electron beam to the mass production of metal nanocatalysts for polymer electrolyte membrane fuel cells (PEMFCs). The amount of dispersants (glycerol) and radical scavengers (isopropyl alcohol, IPA), the most important factors in the electron beam-induced fabrication process, is systematically controlled to find the conditions for the synthesis of the particle structure suitable for PEMFC applications. Furthermore, the effects of the structural changes on the electrochemical properties of the catalysts are thoroughly investigated. Through in-depth studies, it is clearly revealed that while dispersants control the nucleation step of monomers affecting the degree of dispersion of nanoparticles, radical scavengers with strong oxidizing power have an effect on the particle growth rate. Therefore, this study is expected to present the applicability of low-energy electron beam to the mass production of metal nanocatalysts for PEMFCs, and to provide insights into the fabrication of nanoparticles using low-energy electron beams.

Effects of lengthening an aliphatic chain of a phthalimide-based negative redox couple for non-aqueous flow batteries are examined. The working voltage and solubility of N-butylphthalimide are 0.1 V lower and four times greater (2.0 M) than those of methyl-substituted phthalimide. These enhanced properties are attributed to a lower packing density. Consequently, the energy density of the proposed redox couple is greatly enhanced from butyl substitution. Furthermore, the results of the stack flow cell test with N,N,N′,N′-tetramethyl-p-phenylenediamine positive redox couple show advantageous features of this non-aqueous flow battery system: a stable Coulombic efficiency and high working voltage.

We investigated the current and potential spatial distributions and habitable areas of Biston robustum and Camellia japonica in South Korea in order to provide useful data for the conservation of C. japonica and minimize the damage caused by B. robustum. It was predicted that, by 2070, although B. robustum would be widely distributed throughout the Korean Peninsula, except for the western and eastern coastal areas, it would be narrowly distributed along the Sokcho-si and Goseong-gun coastlines in Gangwon Province. C. japonica is currently located along the southern coastline but its critical habitable area is predicted to gradually disappear by 2070. Assessment of the potential distribution probabilities of B. robustum and C. japonica revealed that the area under the curve (AUC) values were 0.995 and 0.991, respectively, which indicate high precision and applicability of the model. Major factors influencing the potential distribution of B. robustum included precipitation of wettest quarter and annual precipitation (BIO16 and BIO12), whereas annual mean temperature and mean temperature of wettest quarter (BIO1 and BIO8) were important variables for explaining C. japonica distribution. Overlapping areas of B. robustum and C. japonica were 11,782 km2, 5447 km2, and 870 km2 for the current, 2050-predicted, and 2070-predicted conditions, respectively, clearly showing a dramatic decrease in area. Although it is predicted that B. robustum would cause continuous damage to C. japonica in the southern part of the Korean Peninsula, such impacts might diminish over time and become negligible in the future.

This study was conducted in an effort to identify the fluctuation of species according to space and time by collecting information on samples of Korean Sphingid moths housed in Korea. The number of Sphingidae moth species housed in Korea was found to be 53. As 48 species and 47 species were found in Gyeongsangnam-do and Gangwon-do, respectively, relatively diverse species were considered to be distributed in these areas. Comparatively, in Jeollabuk-do only 36 species were found, whereas in Chungcheongbuk-do and Jeju Island 39 species were found. The number of individual Sphingid moths surveyed in Korea was 21,414. With regard to the number of individuals per species, Ampelophaga rubiginosa was highest, at 2,483, followed by Theretra japonica (1,716), Callambulyx tatarinovii (1,457), Acosmeryx naga (1,340), Rhagastis mongoliana (1,191), Marumba sperchius (1,083), and Dolbina tancrei (1,072). By region, the largest number of individuals was surveyed in Gyeongsangnam-do (4,595), followed in order by Gangwon-do (3,648 individuals), Gyeonggido (3,011), Jeollanam-do (2,454), and Jeju Island (2,382). Over time, the highest numbers—in this case 9,498 individuals in 52 species—were identified after 2000. In the 1990s, there were 6,027 of 49 species identified; in 1980s, 4,332 individuals of 49 species were identified; and, in the 1970s, 937 individuals of 45 species were identified. It was confirmed that more species and individuals were identified as time passed. The appearance of Sphingid moths by month was found to be as relatively diverse, as they appeared from February to November. Overall, southern species, including M. saishiuana, Cephonodes hylas, Acosmeryx castanea, T. nessus, and T. clotho, which mainly inhabit Jeju Island and the southern part of Korea, have increased over time from the past to the present. Species inhabiting the middle or northern parts of the Korean Peninsula or the entire country of Korea, including Sphingulus mus, Ambulyx schauffelbergeri, and Mimas christophi, show decreases over time.

The biological function of proteins is closely related to its structural motion. For instance, structurally misfolded proteins do not function properly. Although we are able to experimentally obtain structural information on proteins, it is still challenging to capture their dynamics, such as transition processes. Therefore, we need a simulation method to predict the transition pathways of a protein in order to understand and study large functional deformations. Here, we present a new simulation method called normal mode-guided elastic network interpolation (NGENI) that performs normal modes analysis iteratively to predict transition pathways of proteins. To be more specific, NGENI obtains displacement vectors that determine intermediate structures by interpolating the distance between two end-point conformations, similar to a morphing method called elastic network interpolation. However, the displacement vector is regarded as a linear combination of the normal mode vectors of each intermediate structure, in order to enhance the physical sense of the proposed pathways. As a result, we can generate more reasonable transition pathways geometrically and thermodynamically. By using not only all normal modes, but also in part using only the lowest normal modes, NGENI can still generate reasonable pathways for large deformations in proteins. This study shows that global protein transitions are dominated by collective motion, which means that a few lowest normal modes play an important role in this process. NGENI has considerable merit in terms of computational cost because it is possible to generate transition pathways by partial degrees of freedom, while conventional methods are not capable of this.

Since trace amounts of CO in H2 gas produced by steam reforming of methane causes severe poisoning of Pt-based catalysts in polymer electrolyte membrane fuel cells (PEMFCs), research has been mainly devoted to exploring CO-tolerant catalysts. To test the electrochemical property of CO-tolerant catalysts, chronoamperometry is widely used under a CO/H2 mixture gas atmosphere as an essential method. However, in most cases of catalysts with high CO tolerance, the conventional chronoamperometry has difficulty in showing the apparent performance difference. In this study, we propose a facile and precise test protocol to evaluate the CO tolerance via a combination of short-term chronoamperometry and a hydrogen oxidation reaction (HOR) test. The degree of CO poisoning is systematically controlled by changing the CO adsorption time. The HOR polarization curve is then measured and compared with that measured without CO adsorption. When the electrochemical properties of PtRu alloy catalysts with different atomic ratios of Pt to Ru are investigated, contrary to conventional chronoamperometry, these catalysts exhibit significant differences in their CO tolerance at certain CO adsorption times. The present work will facilitate the development of catalysts with extremely high CO tolerance and provide insights into the improvement of electrochemical methods.

Demand on synthetic approaches to high performance electrocatalyst with enhanced durability is increasing for fuel cell applications. In this work, we present a facile synthesis of carbon shell-coated PtFe nanoparticles by using acetylacetonates in metal precursors as carbon sources without an additional polymer coating process for the carbon shell formation. The carbon shell structure is systematically controlled by changing the annealing conditions such as the temperature and gas atmosphere. PtFe catalysts annealed at 700 °C under H2-mixed N2 gas show much higher oxygen reduction reaction (ORR) activity and superior durability compared to a Pt catalyst due to the ultrathin and porous carbon shells. In addition, when increasing the annealing temperature, the carbon shells encapsulating the PtFe nanoparticles improves the durability of the catalysts due to the enhanced crystallinity of the carbon shells. Therefore, it is demonstrated that the developed hybrid catalyst structure with the carbon shells not only allows the access of reactant molecules to the active sites for oxygen reduction reaction but also prevents the agglomeration of metal nanoparticles on carbon supports, even under harsh operating conditions. The proposed synthetic approach and catalyst structure are expected to provide more insights into the development of highly active and durable catalysts for practical fuel cell applications.

In the present study, a numerical framework for predicting the aerodynamic performance and the aeroelastic behavior of floating offshore wind turbine rotor blades involving platform motion was developed. For this purpose, the aerodynamic and structural analyses were conducted simultaneously in a tightly coupled manner by exchanging the information about the aerodynamic loads and the elastic blade deformations at every time step. The elastic behavior of the turbine rotor blades was described by adopting a structural model based on the Euler-Bernoulli beam. The aerodynamic loads by the rotor blades were evaluated by adopting a blade element momentum theory. The numerical simulations were conducted when the platform of the wind turbine independently moves in each of the six degrees-of-freedom directions consisting of heave, sway, surge, roll, pitch, and yaw. It was observed that flexible blades exhibit complicated vibratory behaviors when they are excited by the aerodynamic, inertia, and gravitational forces simultaneously. It was found that the load variation caused by the platform surge or pitch motion has a significant influence on the flapwise and torsional deformations of the rotor blades. The torsional deformation mainly occurs in the nose-down direction, and results in a reduction of the aerodynamic loads. It was also found that the flapwise root bending moment is mainly influenced by the platform surge and pitch motions. On the other hand, the edgewise bending moment is mostly dictated by the gravitational force, but is not affected much by the platform motion.

This research was conducted over a time span of three years, from 2009 to 2011. Twenty-one surveys in total, seven times per year, were done between April and June of each year on major trees on trails around Donghaksa and Gapsa in Mt. Gyeryong National Park in order to identify foodplants of the Lepidoptera larvae and their characteristic appearances. During the survey of Lepidoptera larvae in trees along trails around Donghaksa and Gapsa, 377 individuals and 21 species in 8 families were identified. The 21 species were Alcis angulifera, Cosmia affinis, Libythea celtis, Adoxophyes orana, Amphipyra monolitha, Acrodontis fumosa, Xylena formosa, Ptycholoma lecheana circumclusana, Choristoneura adumbratana, Archips capsigeranus, Pandemis cinnamomeana, Rhopobota latipennis, Apochima juglansiaria, Cifuna locuples, Lymantria dispar, Eilema deplana, Rhodinia fugax, Acronicta rumicis, Amphipyra erebina, Favonius saphirinus, and Dravira ulupi. Twenty-one Lepidoptera insect species were identified in 21 species of trees, including Zelkova serrata. Among them, A. angulifera, C. affinis, and L. celtis were found to have the widest range of foodplants. Additionally, it was found that many species of Lepidoptera insects can utilize more species as foodplants according to the chemical substances in the plants and environments in addition to the foodplants noted in the literature. Also, it is thought that more species can be identified with a survey of various woody and herbaceous plants.

A new modeling method for high voltage direct current (HVDC) systems and associated controllers is presented for the power system simulator for engineering (PSS/E) simulation environment. The aim is to improve the estimation of the transient DC voltage and current in the event of an AC line-to-ground fault. The proposed method consists primary of three interconnected modules for (a) equation conversion; (b) control-mode selection; and (c) DC-line modeling. Simulation case studies were carried out using PSS/E and a power systems computer aided design/electromagnetic transients including DC (PSCAD/EMTDC) model of the Jeju– Haenam HVDC system in Korea. The simulation results are compared with actual operational data and the PSCAD/EMTDC simulation results for an HVDC system during single-phase and three-phase line-to-ground faults, respectively. These comparisons show that the proposed PSS/E modeling method results in the improved estimation of the dynamic variation in the DC voltage and current in the event of an AC network fault, with significant gains in computational efficiency, making it suitable for real-time analysis of HVDC systems.