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An essential requirement for bio/chemical sensors and electronic nose systems is the ability to detect the intended target at room temperature with high selectivity. We report a reduced graphene oxide (rGO)-based gas sensor functionalized with a peptide receptor to detect dinitrotoluene (DNT), which is a byproduct of trinitrotoluene (TNT). We fabricated the multi-arrayed rGO sensor using spin coating and a standard microfabrication technique. Subsequently, the rGO was subjected to photolithography and an etching process, after which we prepared the DNT-specific binding peptide (DNT-bp, sequence: His-Pro-Asn-Phe-Se r-Lys-Tyr-IleLeu-HisGln-Arg-Cys) and DNT non-specific binding peptide (DNT-nbp, sequence: Thr-Ser-Met-Leu-Leu-Met-Ser-Pro-Lys-His-Gln-Ala-Cys). These two peptides were prepared to function as highly specific and highly non-specific (for the control experiment) peptide receptors, respectively. By detecting the differential signals between the DNT-bp and DNT-nbp functionalized rGO sensor, we demonstrated the ability of 2,4-dinitrotoluene (DNT) targets to bind to DNT-specific binding peptide surfaces, showing good sensitivity and selectivity. The advantage of using the differential signal is that it eliminates unwanted electrical noise and/or environmental effects. We achieved sensitivity of 27 ± 2 × 10 −6 per part per billion (ppb) for the slope of resistance change versus DNT gas concentration of 80, 160, 240, 320, and 480 ppm, respectively. By sequentially flowing DNT vapor (320 ppb), acetone (100 ppm), toluene (1 ppm), and ethanol (100 ppm) onto the rGO sensors, the change in the signal of rGO in the presence of DNT gas is 6400 × 10 −6 per ppb whereas the signals from the other gases show no changes, representing highly selective performance. Using this platform, we were also able to regenerate the surface by simply purging with N 2 .

Transportation infrastructure is a fundamental driver of economic growth and regional connectivity; and the supply of this infrastructure is often assumed to reduce spatial disparities. This study investigates the impact of transportation accessibility on regional disparities in land prices across South Korea from 2010 to 2019. Using spatial econometric models and geographically weighted regression (GWR), this study evaluates how variations in transportation networks influence land price differentials between regions. The results confirm that transportation accessibility positively affects land prices; but GWR coefficients reveal substantial regional variations in the extent to which accessibility improvements drive land price growth. Furthermore, while the overall distribution of transportation accessibility remained relatively stable, its influence on land price appreciation varied significantly, contributing to a widening gap in land values between regions. These findings underscore the critical role of transportation infrastructure in shaping regional inequalities and highlight the need for more equitable transportation policies to mitigate spatial disparities and promote balanced regional development

This study aims to generate visually useful imagery by preventing cropping while maintaining resolution and minimizing the degradation of stability and distortion to enhance the stability of a video for Augmented Reality applications. The focus is placed on conducting research that balances maintaining execution speed with performance improvements. By processing Inertial Measurement Unit (IMU) sensor data using the Versatile Quaternion-based Filter algorithm and optical flow, our research first applies motion compensation to frames of input video. To address cropping, PCA-flow-based video stabilization is then performed. Furthermore, to mitigate distortion occurring during the full-frame video creation process, neural rendering is applied, resulting in the output of stabilized frames. The anticipated effect of using an IMU sensor is the production of full-frame videos that maintain visual quality while increasing the stability of a video. Our technique contributes to correcting video shakes and has the advantage of generating visually useful imagery at low cost. Thus, we propose a novel hybrid full-frame video stabilization algorithm that produces full-frame videos after motion compensation with an IMU sensor. Evaluating our method against three metrics, the Stability score, Distortion value, and Cropping ratio, results indicated that stabilization was more effectively achieved with robustness to flow inaccuracy when effectively using an IMU sensor. In particular, among the evaluation outcomes, within the “Turn” category, our method exhibited an 18% enhancement in the Stability score and a 3% improvement in the Distortion value compared to the average results of previously proposed full-frame video stabilization-based methods, including PCA flow, neural rendering, and DIFRINT.

Robots are increasingly employed in diverse services, from room cleaning to coffee preparation, necessitating an accurate understanding of user preferences. Traditional preference-based learning allows robots to learn these preferences through iterative queries about desired behaviors. However, these methods typically assume static human preferences. In this paper, we challenge this static assumption by considering the dynamic nature of human preferences and introduce the discounted preference bandit method to manage these changes. This algorithm adapts to evolving human preferences and supports seamless human–robot interaction through effective query selection. Our approach outperforms existing methods in time-varying scenarios across three key performance metrics.

This study aims to investigate a fundamental yet largely overlooked question: “Does investing in transportation infrastructure positively impact farms’ agricultural gross income?” It is examined based on the role of transportation infrastructure in ensuring equal access to market opportunities in the context of the widening regional economic disparity in Korea. The main novelty of this study lies in its attempt to introduce an accessibility measure for evaluating the benefits of transportation infrastructure in a rural setting, which has been limitedly applied in urban-centered studies. To accomplish this task, multilevel and spatial econometric models were employed to evaluate the ex-post impact of transportation accessibility on agricultural gross income from the perspectives of farmers, primarily, and rural autonomies, subsequently. This study found that the continuation of the current direction of transportation policy—without substantial consideration for agriculture as an industry and rural areas as living spaces—can intensify the economic alienation of agriculture and rural areas. This study concludes that opportunities for market access provided by the immense public investments in transportation infrastructure should be fairly distributed to farmers and rural autonomies to promote balanced regional development in Korea.

Instance segmentation has gained attention in various computer vision fields, such as autonomous driving, drone control, and sports analysis. Recently, many successful models have been developed, which can be classified into two categories: accuracy- and speed-focused. Accuracy and inference time are important for real-time applications of this task. However, these models just present inference time measured on different hardware, which makes their comparison difficult. This study is the first to evaluate and compare the performances of state-of-the-art instance segmentation models by focusing on their inference time in a fixed experimental environment. For precise comparison, the test hardware and environment should be identical; hence, we present the accuracy and speed of the models in a fixed hardware environment for quantitative and qualitative analyses. Although speed-focused models run in real-time on high-end GPUs, there is a trade-off between speed and accuracy when the computing power is insufficient. The experimental results show that a feature pyramid network structure may be considered when designing a real-time model, and a balance between the speed and accuracy must be achieved for real-time application.

We focus on firms' risk-taking for customers and investigate how this type of risk-taking is influenced by \"performance relative to aspiration\" (PRA). Specifically, we examine the PRA and project-financing (PF) loan activities of all 79 savings banks in South Korea from 2013 to 2021. Contrary to the prevalent prediction that risk-taking increases with negative PRA, we demonstrate that banks with a more positive PRA take higher risk through PF loans. This suggests that a more positive PRA has given banks the freedom of action to pursue a social-based goal of small business support, which has been neglected by management as less important than their most important goal of pursuing their own profit. Furthermore, unlike the dominant idea that family firms are more risk-averse than non-family firms, we find that when PRA is positive, family firms engage in risky actions more actively for their customers than non-family counterparts. In other words, banks with better PRA take more risk to provide financial opportunities to their customers and this type of risk-taking is more pronounced for family firms. Implications and future research are discussed.

In this study, we propose a multi-scale ensemble learning method for thermal image enhancement in different image scale conditions based on convolutional neural networks. Incorporating the multiple scales of thermal images has been a tricky task so that methods have been individually trained and evaluated for each scale. However, this leads to the limitation that a network properly operates on a specific scale. To address this issue, a novel parallel architecture leveraging the confidence maps of multiple scales have been introduced to train a network that operates well in varying scale conditions. The experimental results show that our proposed method outperforms the conventional thermal image enhancement methods. The evaluation is presented both quantitatively and qualitatively.

Traditional economics-based framework suggests that firm cooperates with competitors to increase its market power or efficiency in transaction for the maximization of its self-interest profit. However, nowadays growing numbers of firm engage in alliance with competitors for non-economic purpose. This paper seeks to understand the nature of inter-firm alliance between direct competitors by discussing several critical issues regarding it. The issues are chosen by the criterion that useful perspectives from either organization theory or strategic management can be applied to this phenomenon so that scholars are encouraged and can be easier to do a research on this topic in the future. In this regard, I seek to answer the question of why firm cooperates with competitor by comparatively adopting four novel approaches, which, combined together, provide an excellent complementary view to the traditional economics-based approach. Also, by understanding the distinctive feature of decision-making process when firm conduct a collaboration with competitor this study provides a practical insight on how firm structures, manages, and makes a decision when it cooperates with competitors. Overall, several conceptual ideas suggested by this paper can be an interesting starting point for the future empirical research.

Background/Objectives: Accumulating evidence and clinical observations suggest that the gut microbiome plays a crucial role in functional dyspepsia (FD). However, the precise characterization of this relationship is unclear. This systematic review and meta-analysis aimed to elucidate the potential role of the gut microbiome in FD based on evidence from published clinical studies. Methods: A comprehensive search of three databases (PubMed, Google Scholar, and Web of Science) was conducted, and 17 relevant clinical studies, including 8 observational studies and 9 interventional studies, published up to September 2025, were identified. Data on the gut microbiome and FD were extracted and subjected to meta-analysis. Results: Meta-analysis revealed no significant differences in gut microbiota α- or β-diversity between patients with FD and healthy controls (Shannon index: standardized mean difference [SMD] = −0.12, 95% confidence interval [CI] −0.90 to 0.67, I2 = 88%). In contrast, effective interventions induced notable shifts in the microbial community structure (pooled SMD = 0.27, 95% CI −0.28 to −0.83, I2 = 58%). These shifts were accompanied by increased short-chain fatty acid (SCFA) production and intestinal tight-junction protein levels, which coincided with improved FD symptoms. Conclusions: Although no significant differences in the gut microbiota were detected between patients with FD and healthy controls, interventions in patients with FD induced marked changes in the microbial community. Modulation of gut microbiota-related metabolites, such as SCFAs, may represent a promising therapeutic strategy for the management of FD.