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Thin-film composite (TFC) membranes containing various fillers and additives present an effective alternative to conventional dense polymer membranes, which often suffer from low permeance (flux) and the permeability–selectivity tradeoff. Alongside the development and utilization of numerous new polymers over the past few decades, diverse additives such as metal–organic frameworks (MOFs), graphene oxides (GOs), and ionic liquids (ILs) have been integrated into the polymer matrix to enhance performance. However, achieving desirable interfacial compatibility between these additives and the host polymer matrix, particularly in TFC structures, remains a significant challenge. This review discusses recent advancements in TFC membranes for CO2/N2 separation, focusing on material structure, polymer–additive interaction, interface and separation properties. Specifically, we examine membranes operating under dry conditions to clearly assess the impact of additives on membrane properties and performance. Additionally, we provide a perspective on future research directions for designing high-performance membrane materials.

In this study, we propose a fire classification system using image clustering based on a federated learning (FL) structure. This system enables fire detection in various industries, including manufacturing. The accurate classification of fire, smoke, and normal conditions is an important element of fire prevention and response systems in industrial sites. The server in the proposed system extracts data features using a pretrained vision transformer model and clusters the data using the bisecting K-means algorithm to obtain weights. The clients utilize these weights to cluster local data with the K-means algorithm and measure the difference in data distribution using the Kullback–Leibler divergence. Experimental results show that the proposed model achieves nearly 99% accuracy on the server, and the clustering accuracy on the clients remains high. In addition, the normalized mutual information value remains above 0.6 and the silhouette score reaches 0.9 as the rounds progress, indicating improved clustering quality. This study shows that the accuracy of fire classification is enhanced by using FL and clustering techniques and has a high potential for real-time detection.

Background and Objectives: Retrograde intrarenal surgery (RIRS) is a minimally invasive technique for nephrolithiasis. RIRS is performed via a monitor screen displaying a magnified surgical site. Respiration can affect the stability of the surgical view during RIRS because the kidneys are close to the diaphragm. The purpose of this trial is to compare the effect of anesthesia type on the stability of the surgical view during RIRS between spinal anesthesia and general anesthesia. Materials and Methods: Patients were allocated to the general anesthesia group or spinal anesthesia group. During surgery, movement of the surgical field displayed on the monitor screen was graded by the first assistant on a 10-grade numeric rating scale (0–10). Next, it was also graded by the main surgeon. After surgery, we evaluated the discomfort with the anesthesia method for all patients. Results: Thirty-four patients were allocated to the general anesthesia group and 32 patients to the spinal anesthesia group. The average values of the two surgeons for surgical field oscillation grade showed vision on the monitor screen was more stable in the general anesthesia group than the spinal anesthesia group (3.3 ± 1.6 vs. 5.0 ± 1.6, p < 0.001). The degrees of the inconvenience of the surgery did not differ between the groups (0.7 ± 1.8 vs. 1.6 ± 2.6, p = 0.114), even though more patients reported inconvenience with a grade of 3 or more in the spinal anesthesia group (8.8% vs. 28.1%, p = 0.042). Conclusions: In terms of the visualization of the surgical site, general anesthesia might provide a more stable surgical view during RIRS compared to spinal anesthesia without increasing inconvenience induced by the type of anesthesia.

Metabolic dysfunction is commonly observed in schizophrenia spectrum disorders (SSDs). The causes of metabolic comorbidity in SSDs are complex and include intrinsic or biological factors linked to the disorder, which are compounded by antipsychotic (AP) medications. The exact mechanisms underlying SSD pathophysiology and AP-induced metabolic dysfunction are unknown, but dysregulated lipid metabolism may play a role. Lipidomics, which detects lipid metabolites in a biological sample, represents an analytical tool to examine lipid metabolism. This systematic review aims to determine peripheral lipid signatures that are dysregulated among individuals with SSDs (1) with minimal exposure to APs and (2) during AP treatment. To accomplish this goal, we searched MEDLINE, Embase, and PsychINFO databases in February 2024 to identify all full-text articles written in English where the authors conducted lipidomics in SSDs. Lipid signatures reported to significantly differ in SSDs compared to controls or in relation to AP treatment and the direction of dysregulation were extracted as outcomes. We identified 46 studies that met our inclusion criteria. Most of the lipid metabolites that significantly differed in minimally AP-treated patients vs. controls comprised glycerophospholipids, which were mostly downregulated. In the AP-treated group vs. controls, the significantly different metabolites were primarily fatty acyls, which were dysregulated in conflicting directions between studies. In the pre-to-post AP-treated patients, the most impacted metabolites were glycerophospholipids and fatty acyls, which were found to be primarily upregulated and conflicting, respectively. These lipid metabolites may contribute to SSD pathophysiology and metabolic dysfunction through various mechanisms, including the modulation of inflammation, cellular membrane permeability, and metabolic signaling pathways.

Malicious reverse engineering of software has served as a valuable technique for attackers to infringe upon and steal intellectual property. We can employ obfuscation techniques to protect against such attackers as useful tools to safeguard software. Applying obfuscation techniques to source code can prevent malicious attackers from reverse engineering a program. However, the ambiguity surrounding the protective efficacy of these source code obfuscation tools and techniques presents challenges for users in evaluating and comparing the varying degrees of protection provided. This paper addresses these issues and presents a methodology to quantify the effect of source code obfuscation. Our proposed method is based on three main types of data: (1) the control flow graph, (2) the program path, and (3) the performance overhead added to the process—all of which are derived from a program analysis conducted by human experts and automated tools. For the first time, we have implemented a tool that can quantitatively evaluate the quality of obfuscation techniques. Then, to validate the effectiveness of the implemented framework, we conducted experiments using four widely recognized commercial and open-source obfuscation tools. Our experimental findings, based on quantitative values related to obfuscation techniques, demonstrate that our proposed framework effectively assesses obfuscation quality.

In an optical system with multiple lens groups and increased zoom magnification levels, achieving a smooth zoom locus is increasingly difficult. Traditional methods for calculating zoom loci often involve complex and time-consuming formulas. Consequently, we utilized the Padé approximation in optical design software to compute the zoom locus analytically, irrespective of the number of zoom positions (nodes). The initial data were used to assign orders to rational function polynomials, facilitating Padé approximation. If the image surface extended beyond the depth of focus (DOF), a node was added, with adjustments made until it fell within the DOF range. Furthermore, Padé approximation was performed to prevent singularities. The loci of all lens groups in the optical system can be expressed in a rational function format. Specifically, the numerator and denominator polynomial degrees were 20° and 1°, respectively, with their sum being the total number of nodes. In addition, we calculated the zoom locus by increasing the numerator sequence to minimize the occurrence of the singularity and added the node automatically to enable zoom locus calculation in all optical systems. Accordingly, we could make fast calculations, unlike conventional methods, using complex and time-consuming simultaneous equations. Therefore, we could express the locus of the compensated group in the form of a smooth function, as presently shown.

Thin-film photodiodes (TFPD) monolithically integrated on the Si Read-Out Integrated Circuitry (ROIC) are promising imaging platforms when beyond-silicon optoelectronic properties are required. Although TFPD device performance has improved significantly, the pixel development has been limited in terms of noise characteristics compared to the Si-based image sensors. Here, a thin-film-based pinned photodiode (TF-PPD) structure is presented, showing reduced kTC noise and dark current, accompanied with a high conversion gain (CG). Indium-gallium-zinc oxide (IGZO) thin-film transistors and quantum dot photodiodes are integrated sequentially on the Si ROIC in a fully monolithic scheme with the introduction of photogate (PG) to achieve PPD operation. This PG brings not only a low noise performance, but also a high full well capacity (FWC) coming from the large capacitance of its metal-oxide-semiconductor (MOS). Hence, the FWC of the pixel is boosted up to 1.37 Me- with a 5 μm pixel pitch, which is 8.3 times larger than the FWC that the TFPD junction capacitor can store. This large FWC, along with the inherent low noise characteristics of the TF-PPD, leads to the three-digit dynamic range (DR) of 100.2 dB. Unlike a Si-based PG pixel, dark current contribution from the depleted semiconductor interfaces is limited, thanks to the wide energy band gap of the IGZO channel material used in this work. We expect that this novel 4 T pixel architecture can accelerate the deployment of monolithic TFPD imaging technology, as it has worked for CMOS Image sensors (CIS).

This study aimed to analyze the competitive relationship among meetings, incentive, convention, and exhibition (MICE) destinations with reference to the notion of niche businesses in New Zealand, and to explore the existence of cooperative strategies between neighboring destinations. The data were collected from the Ministry of Business, Innovation, and Employment Reports on the Convention Activity Survey (CAS) in New Zealand. The study contributes towards an innovative and better understanding of the dynamics of sustainable destination competitiveness. It broadens the scope of MICE industry research by exploring new insights on the notion of destination competition and makes a theoretical connection between niche theory and the importance of coopetition. By investigating the case of MICE destinations of four central cities in New Zealand, this study provides information on the strategic significance of niche marketing for global destinations preparing for entrance into this market.

Angioleiomyoma, a rare variant of leiomyoma, is a benign tumor of mesenchymal origin. Angioleiomyomas of the female urogenital tract are extremely rare, with only six cases of uterine cervical angioleiomyoma previously reported in the literature. In this case study, we report on a 49-year-old female patient who presented with menorrhagia whose initial magnetic resonance imaging (MRI) findings suggested cervical squamous cell carcinoma (SCC). However, following the hysterectomy, histological examination confirmed the lesion to be angioleiomyoma. To the best of our knowledge, there have been no previously reported cases of angioleiomyomas presenting with MRI findings that are suggestive of uterine SCC. Recognizing that angioleiomyomas can mimic uterine malignancies on MRI may prove beneficial for future diagnostic and treatment strategies.

Sleep is an essential component of quality of life. The majority of people experience sleep problems that impact their quality of life. Melatonin is currently a representative sleep aid. However, it is classified as a prescription drug in most countries, and consumers cannot purchase it to improve their sleep. This sleep induction experiment in mice aimed to identify a natural combination product (NCP) that can create synergistic sleep-promoting effects. Based on the mechanism of action of sleep, we investigated whether phenomenological indicators of sleep quality change according to the intake of NCP. The sleep onset and sleep time of the mice that consumed the NCP found by this study were improved compared to the existing sleep aids. The mean melatonin level in the blood increased by 197% compared to the control. To our knowledge, this is the first study to demonstrate that Rosa multiflora Thunb. (Yeongsil) can promote sleep similarly to Zizyphus jujuba Miller (Sanjoin). The results indicate a preclinical study of NCPs containing Rosa multiflora Thunb and Zizyphus jujuba Miller developed by us showed significant differences in sleep incubation and duration depending on melatonin concentrations. Our results also suggest that increased melatonin concentrations in the blood are likely to improve sleep quality, especially regarding incubation periods.

DDX3 is a DEAD-box RNA helicase with diverse biological functions through multicellular pathways. The objective of this study was to investigate the role of DDX3 in regulating melanogenesis by the exploring signaling pathways involved. Various concentrations of hydrogen peroxide were used to induce melanogenesis in SK-Mel-2 human melanoma cells. Melanin content assays, tyrosinase activity analysis, and Western blot analysis were performed to determine how DDX3 was involved in melanogenesis. Transient transfection was performed to overexpress or silence DDX3 genes. Immunoprecipitation was performed using an antityrosinase antibody. Based on the results of the cell viability test, melanin content, and activity of tyrosinase, a key melanogenesis enzyme, in SK-Mel-2 human melanoma cells, hydrogen peroxide at 0.1 mM was chosen to induce melanogenesis. Treatment with H2O2 notably increased the promoter activity of DDX3. After treatment with hydroperoxide for 4 h, melanin content and tyrosinase activity peaked in DDX3-transfected cells. Overexpression of DDX3 increased melanin content and tyrosinase expression under oxidative stress induced by H2O2. DDX3 co-immunoprecipitated with tyrosinase, a melanogenesis enzyme. The interaction between DDX3 and tyrosinase was strongly increased under oxidative stress. DDX3 could increase melanogenesis under the H2O2-treated condition. Thus, targeting DDX3 could be a novel strategy to develop molecular therapy for skin diseases.

Myostatin is a member of the transforming growth factor-beta superfamily and is an endogenous negative regulator of muscle growth. This study aimed to determine whether an oral administration of Lactobacillus casei expressing modified human myostatin (BLS-M22) could elicit sufficient levels of myostatin-specific antibody and improve the dystrophic features of an animal model of Duchenne muscular dystrophy (DMD; mdx mouse). BLS-M22 is a recombinant L. casei engineered to harbor the pKV vector and poly-gamma-glutamic acid gene linked to a modified human myostatin gene. Serological analysis showed that anti-myostatin IgG titers were significantly increased, and serum creatine kinase was significantly reduced in the BLS-M22-treated mdx mice compared to the control mice. In addition, treatment of BLS-M22 resulted in a significant increase in body weight and motor function (Rotarod behavior test). Histological analysis showed an improvement in the dystrophic features (fibrosis and muscle hypertrophy) of the mdx mice with the administration of BLS-M22. The circulating antibodies generated after BLS-M22 oral administration successfully lowered serum myostatin concentration. Myostatin blockade resulted in serological, histological, and functional improvements in mdx mice. Overall, the findings suggest the potential of BLS-M22 to treat DMD; however, further clinical trials are essential to ascertain its efficacy and safety in humans.

(1) Background: The N-methyl-D-aspartate receptors (NMDARs) mediate fast excitatory currents leading to depolarization. Postsynaptic NMDARs are ionotropic glutamate receptors that mediate excitatory glutamate or glycine signaling in the CNS and play a primary role in long-term potentiation, which is a major form of use-dependent synaptic plasticity. The overstimulation of NMDARs mediates excessive Ca2+ influx to postsynaptic neurons and facilitates more production of ROS, which induces neuronal apoptosis. (2) Methods: To confirm the induced inward currents by the coapplication of glutamate and ergotamine on NMDARs, a two-electrode voltage clamp (TEVC) was conducted. The ergotamine-mediated inhibitory effects of NR1a/NR2A subunits were explored among four different kinds of recombinant NMDA subunits. In silico docking modeling was performed to confirm the main binding site of ergotamine. (3) Results: The ergotamine-mediated inhibitory effect on the NR1a/NR2A subunits has concentration-dependent, reversible, and voltage-independent properties. The major binding sites were V169 of the NR1a subunit and N466 of the NR2A subunit. (4) Conclusion: Ergotamine effectively inhibited NR1a/NR2A subunit among the subtypes of NMDAR. This inhibition effect can prevent excessive Ca2+ influx, which prevents neuronal death.

This review addresses polymer microspheres used as adsorbent for wastewater treatment. The removal of various pollutants (including dyes, heavy metal ions, and organic pollutants) is a prominent issue, as they can cause severe health problems. Porous microspheres can provide large specific area and active sites for adsorption or photo degradation. Enhancement in performance is achieved by various modifications, such as the introduction of nanoparticles, magnetic particles, and ZIF-8. Some microspheres were synthesized from synthetic polymers such as vinylic polymer and polydopamine (PDA) through a facile fabrication process. Natural polymers (such as cellulose, alginate, and chitosan) that are biodegradable and eco-friendly are also used. The adsorbents used in industrial application require high adsorption capacity, thermal stability, and recyclability. Batch adsorption experiments were conducted to investigate the optimal conditions, influence of related factors, and adsorption capacities. Insights regarding the adsorption mechanisms were given from the kinetic model, isotherm model, and various characterization methods. The recyclability is investigated through regeneration ratio, or their maintenance of their capability through repeated adsorption-desorption cycles. The high potential of polymer microsphere for the removal of pollutants from wastewater is shown through the high adsorption capacities, environmentally friendliness, and high stability.

Currently, more than 30% of the fine dust generated in the Seoul metropolitan area is a pollutant emitted from automobiles such as diesel vehicles, and air pollution caused by this is becoming increasingly serious. In addition, the importance of electric vehicle distribution is increasing due to the strengthening of international environmental regulations on automobile exhaust gas and increasing the possibility of depletion of petroleum resources. This manuscript proposes a method for selecting an optimal electric vehicle charging station location in expanding charging facilities to activate electric vehicle distribution. For the sake of illustration, directions will be provided on how to select the best location for electric vehicle charging stations using data from Seoul, which has the best access. As the features, the number of living population and work force people and the number of guest facilities, which are determined to affect demand for quick charging, are considered. The missing values of the observed data are imputed based on the kriging technique from spatial correlation, and by segmenting the data through clustering, a representative technique of unsupervised learning, the characteristics of each cluster are examined and the characteristics of the clusters are identified. In addition, machine learning techniques such as the elastic net, random forest, support vector machine, and extreme gradient boosting are applied to examine the influence of the features used in predicting classes of data. In clustering analysis, the optimal number of clusters was determined to be 3 based on the heuristic and information-theoretic methods, and all the machine learning techniques considered showed that the number of work force population is the most important feature in predicting classes of data. All things considered from our results, it is reasonable to install quick electric vehicle charging stations in the places with the highest concentration of work force population and guest facility.

Public bike-sharing is eco-friendly, connects excellently with other transportation modes, and provides a means of mobility that is highly suitable in the current era of climate change. This study proposes a methodology for inferring the bike trip purpose based on bike-share and point-of-interest (POI) data. Because the purpose of a trip involves decision-making, its inference necessitates an understanding of the spatiotemporal complexity of human activities. Thus, the spatiotemporal features affecting bike trips were selected from the bike-share data, and the land uses at the origin and destination of the trips were extracted from the POI data. During POI type embedding, the data were augmented considering the geographical distance between the POIs and the number of bike rentals at each bike station. We further developed a ground truth data construction method that uses temporal mobile and POI data. The inference model was built using machine learning and applied to experiments involving bike stations in Seocho-gu, Seoul, Korea. The experimental results revealed that optimal performance was achieved with the use of decision tree algorithms, as demonstrated by a 78.95% overall accuracy and 66.43% F1-score. The proposed method contributes to a better understanding of the causes of movement within cities.

Melanoma accounts for the majority of skin cancer deaths. About 50% of all melanomas are associated with BRAF mutations. BRAF mutations are classified into three classes with regard to dependency on RAF dimerization and RAS signaling. The most frequently occurring class I BRAF V600 mutations are sensitive to vemurafenib whereas class II and class III mutants, non-V600 BRAF mutants are resistant to vemurafenib. Herein we report six pyrimido[4,5-d]pyrimidin-2-one derivatives possessing highly potent anti-proliferative activities on melanoma cells harboring BRAF class I/II/III mutants. Novel and most potent derivative, SIJ1777, possesses not only two-digit nanomolar potency but also 2 to 14-fold enhanced anti-proliferative activities compared with reference compound, GNF-7 against melanoma cells (SK-MEL-2, SK-MEL-28, A375, WM3670, WM3629). Moreover, SIJ1777 substantially inhibits the activation of MEK, ERK, and AKT and remarkably induces apoptosis and significantly blocks migration, invasion, and anchorage-independent growth of melanoma cells harboring BRAF class I/II/II mutations while both vemurafenib and PLX8394 have little to no effects on melanoma cells expressing BRAF class II/III mutations. Taken together, our six GNF-7 derivatives exhibit highly potent activities against melanoma cells harboring class I/II/III BRAF mutations compared with vemurafenib as well as PLX8394.

In this work, multivariate heterogeneous autoregressive-realized volatility (HAR-RV) models are discussed with their least squares estimations. We consider multivariate HAR models of order p with q multiple assets to explore the relationships between two or more assets’ volatility. The strictly stationary solution of the HAR(p,q) model is investigated as well as the asymptotic normality theories of the least squares estimates are established in the cases of i.i.d. and correlated errors. In addition, an exponentially weighted multivariate HAR model with a common decay rate on the coefficients is discussed together with the common rate estimation. A Monte Carlo simulation is conducted to validate the estimations: sample mean and standard error of the estimates as well as empirical coverage and average length of confidence intervals are calculated. Lastly, real data of volatility of Gold spot price and S&P index are applied to the model and it is shown that the bivariate HAR model fitted by selected optimal lags and estimated coefficients is well matched with the volatility of the financial data.

Metal Additive Manufacturing and Laser Powder Bed Fusion (LPBF), in particular, have come forth in recent years as an outstanding innovative manufacturing approach. The LPBF process is notably characterized by very high solidification and cooling rates, as well as repeated abrupt heating and cooling cycles, which generate the build-up of anisotropic microstructure and residual stresses. Post-processing stress-relieving heat treatments at elevated temperatures are often required in order to release some of these stresses. The effects of 1 h–hold heat treatments at different specific temperatures (solutionizing, annealing, stress-relieve and low-temperature stress-relieve) on residual stress levels together with microstructure characterization were therefore investigated for the popular Alloy 625 produced by LPBF. The build-up of residual stress is accommodated by the formation of dislocations that produce local crystallographic misorientation within grains. Electron backscattered diffraction (EBSD) was used to investigate local misorientation by means of orientation imaging, thereby assessing misorientation or strain levels, in turn representing residual stress levels within the material. The heavily constrained as-built material was found to experience full recrystallization of equiaxed grains after solutionizing at 1150 °C, accompanied by significant drop of residual stress levels due to this grains reconfiguration. Heat treatments at lower temperatures however, even as high as the annealing temperature of 980 °C, were found to be insufficient to promote recrystallization though effective to some extent to release residual stress through apparently dislocations recovery. Average misorientation data obtained by EBSD were found valuable to evaluate qualitatively residual stress levels. The effects of the different heat treatments are discussed and suggest that the peculiar microstructure of alloys produced by LPBF can possibly be transformed to suit specific applications.

In the Internet of Things (IoT) systems, it is often required to deliver a secure message to a group of devices. The public key broadcast encryption is an efficient primitive to handle IoT broadcasts, by allowing a user (or a device) to broadcast encrypted messages to a group of legitimate devices. This paper proposes an IoT-friendly subset representation called Combinatorial Subset Difference (CSD), which generalizes the existing subset difference (SD) method by allowing wildcards (*) in any position of the bitstring. Based on the CSD representation, we first propose an algorithm to construct the CSD subset, and a CSD-based public key broadcast encryption scheme. By providing the most general subset representation, the proposed CSD-based construction achieves a minimal header size among the existing broadcast encryption. The experimental result shows that our CSD saves the header size by 17% on average and more than 1000 times when assuming a specific IoT example of IP address with 20 wildcards and 2 20 total users, compared to the SD-based broadcast encryption. We prove the semantic security of CSD-based broadcast encryption under the standard l-BDHE assumption, and extend the construction to a chosen-ciphertext-attack (CCA)-secure version.

Investigative systems for purified membrane transporters are almost exclusively reliant on the use of phospholipid vesicles or liposomes. Liposomes provide an environment to support protein function; however, they also have numerous drawbacks and should not be considered as a “one-size fits all” system. The use of artificial vesicles comprising block co-polymers (polymersomes) offers considerable advantages in terms of structural stability; provision of sufficient lateral pressure; and low passive permeability, which is a particular issue for transport assays using hydrophobic compounds. The present investigation demonstrates strategies to reconstitute ATP binding cassette (ABC) transporters into hybrid vesicles combining phospholipids and the block co-polymer poly (butadiene)-poly (ethylene oxide). Two efflux pumps were chosen; namely the Novosphingobium aromaticivorans Atm1 protein and human P-glycoprotein (Pgp). Polymersomes were generated with one of two lipid partners, either purified palmitoyl-oleoyl-phosphatidylcholine, or a mixture of crude E. coli lipid extract and cholesterol. Hybrid polymersomes were characterised for size, structural homogeneity, stability to detergents, and permeability. Two transporters, NaAtm1 and P-gp, were successfully reconstituted into pre-formed and surfactant-destabilised hybrid polymersomes using a detergent adsorption strategy. Reconstitution of both proteins was confirmed by density gradient centrifugation and the hybrid polymersomes supported substrate dependent ATPase activity of both transporters. The hybrid polymersomes also displayed low passive permeability to a fluorescent probe (calcein acetomethoxyl-ester (C-AM)) and offer the potential for quantitative measurements of transport activity for hydrophobic compounds.

This study proposes an asymmetric interface that can provide head-mounted display (HMD) and non-HMD users with improved presence and an experience consistent with the user’s environment in an asymmetric virtual reality. For the proposed asymmetric interface, a controller-based hand interface is designed for portability, easy and convenient use, and high immersion. Subsequently, a three-step decision-making structure that supports accurate and efficient decision-making is defined based on the asymmetric experience structure of each user (HMD and non-HMD). Based on this process, an optimal interface that distinguishes between HMD (direct interaction) and non-HMD users (multi-viewpoint interaction) is implemented. With the objective of surveying and analyzing each user’s experience along with the presence provided by the proposed interface, an asymmetric virtual reality application is developed directly, and an experiment is conducted with the participants. Finally, it is statistically analyzed and verified that the use of the proposed asymmetric interface can provide optimal presence and user-optimized experience to both HMD and non-HMD users.

In this paper, we propose a new temperature prediction model based on deep learning by using real observed weather data. To this end, a huge amount of model training data is needed, but these data should not be defective. However, there is a limitation in collecting weather data since it is not possible to measure data that have been missed. Thus, the collected data are apt to be incomplete, with random or extended gaps. Therefore, the proposed temperature prediction model is used to refine missing data in order to restore missed weather data. In addition, since temperature is seasonal, the proposed model utilizes a long short-term memory (LSTM) neural network, which is a kind of recurrent neural network known to be suitable for time-series data modeling. Furthermore, different configurations of LSTMs are investigated so that the proposed LSTM-based model can reflect the time-series traits of the temperature data. In particular, when a part of the data is detected as missing, it is restored by using the proposed model’s refinement function. After all the missing data are refined, the LSTM-based model is retrained using the refined data. Finally, the proposed LSTM-based temperature prediction model can predict the temperature through three time steps: 6, 12, and 24 h. Furthermore, the model is extended to predict 7 and 14 day future temperatures. The performance of the proposed model is measured by its root-mean-squared error (RMSE) and compared with the RMSEs of a feedforward deep neural network, a conventional LSTM neural network without any refinement function, and a mathematical model currently used by the meteorological office in Korea. Consequently, it is shown that the proposed LSTM-based model employing LSTM-refinement achieves the lowest RMSEs for 6, 12, and 24 h temperature prediction as well as for 7 and 14 day temperature prediction, compared to other DNN-based and LSTM-based models with either no refinement or linear interpolation. Moreover, the prediction accuracy of the proposed model is higher than that of the Unified Model (UM) Local Data Assimilation and Prediction System (LDAPS) for 24 h temperature predictions.

This study proposes the interaction of third-person virtual reality (TPVR) to provide users with a new presence and experience during immersive virtual reality (VR). The proposed TPVR interaction consists of the ability to directly control the virtual environment and objects by utilizing the hands. For this purpose, this study defines the interaction of TPVR from three types: moving inside the virtual environment, selecting virtual objects and using a menu interface that includes a variety of functions and behaviors. Then, it designs its own experimental application that is able to systematically compare and analyze the proposed interaction against traditional first-person perspective VR. Finally, it examines whether the proposed interaction provides new presence and experience to users by conducting survey experiments. Through this process, this study was able to confirm not only that TPVR interaction can provide a sufficient presence, even in comparison with the first-person viewpoint, but also that it causes less VR sickness and offers a differentiated VR experience, suggesting that the proposed interaction can provide an alternative method for utilizing VR technology in a greater variety of fields.

This research proposes a gaze pointer-based user interface to provide user-oriented interaction suitable for the virtual reality environment on mobile platforms. For this purpose, a mobile platform-based three-dimensional interactive content is produced to test whether the proposed gaze pointer-based interface increases user satisfaction through the interactions in a virtual reality environment based on mobile platforms. The gaze pointer-based interface—the most common input method for mobile virtual reality content—is designed by considering four types: the visual field range, the feedback system, multi-dimensional information transfer, and background colors. The performance of the proposed gaze pointer-based interface is analyzed by conducting experiments on whether or not it offers motives for user interest, effects of enhanced immersion, provision of new experience, and convenience in operating content. In addition, it is verified whether any negative psychological factors, such as VR sickness, fatigue, difficulty of control, and discomfort in using contents are caused. Finally, through the survey experiment, this study confirmed that it is possible to design different ideal gaze pointer-based interface in mobile VR environment according to presence and convenience.