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Crop diseases cannot be accurately predicted by merely analyzing individual disease causes. Only through construction of a comprehensive analysis system can users be provided with predictions of highly probable diseases. In this study, cloud-based technology capable of handling the collection, analysis, and prediction of agricultural environment information in one common platform was developed. The proposed Farm as a Service (FaaS) integrated system supports high-level application services by operating and monitoring farms as well as managing associated devices, data, and models. This system registers, connects, and manages Internet of Things (IoT) devices and analyzes environmental and growth information. In addition, the IoT-Hub network model was constructed in this study. This model supports efficient data transfer for each IoT device as well as communication for non-standard products, and exhibits high communication reliability even in poor communication environments. Thus, IoT-Hub ensures the stability of technology specialized for agricultural environments. The integrated agriculture-specialized FaaS system implements specific systems at different levels. The proposed system was verified through design and analysis of a strawberry infection prediction system, which was compared with other infection models.

Kubernetes, an open-source container orchestration platform, enables high availability and scalability through diverse autoscaling mechanisms such as Horizontal Pod Autoscaler (HPA), Vertical Pod Autoscaler and Cluster Autoscaler. Amongst them, HPA helps provide seamless service by dynamically scaling up and down the number of resource units, called pods, without having to restart the whole system. Kubernetes monitors default Resource Metrics including CPU and memory usage of host machines and their pods. On the other hand, Custom Metrics, provided by external software such as Prometheus, are customizable to monitor a wide collection of metrics. In this paper, we investigate HPA through diverse experiments to provide critical knowledge on its operational behaviors. We also discuss the essential difference between Kubernetes Resource Metrics (KRM) and Prometheus Custom Metrics (PCM) and how they affect HPA’s performance. Lastly, we provide deeper insights and lessons on how to optimize the performance of HPA for researchers, developers, and system administrators working with Kubernetes in the future.

Pork production is hugely impacted by the health and breeding of pigs. Analyzing the eating pattern of pigs helps in optimizing the supply chain management with a healthy breeding environment. Monitoring the feed intake of pigs in a barn provides information about their eating habits, behavioral patterns, and surrounding environment, which can be used for further analysis to monitor growth in pigs and eventually contribute to the quality and quantity of meat production. In this paper, we present a novel method to estimate the number of pigs taking in feed by considering the pig’s posture. In order to solve problems arising from using the pig’s posture, we propose an algorithm to match the pig’s head and the corresponding pig’s body using the major-and-minor axis of the pig detection box. In our experiment, we present the detection performance of the YOLOv5 model according to the anchor box, and then we demonstrate that the proposed method outperforms previous methods. We therefore measure the number of pigs taking in feed over a period of 24 h and the number of times pigs consume feed in a day over a period of 30 days, and observe the pig’s feed intake pattern.

With the exponential growth of the Internet of Things (IoT), edge computing is in the limelight for its ability to quickly and efficiently process numerous data generated by IoT devices. EdgeX Foundry is a representative open-source-based IoT gateway platform, providing various IoT protocol services and interoperability between them. However, due to the absence of container orchestration technology, such as automated deployment and dynamic resource management for application services, EdgeX Foundry has fundamental limitations of a potential edge computing platform. In this paper, we propose EdgeX over Kubernetes, which enables remote service deployment and autoscaling to application services by running EdgeX Foundry over Kubernetes, which is a product-grade container orchestration tool. Experimental evaluation results prove that the proposed platform increases manageability through the remote deployment of application services and improves the throughput of the system and service quality with real-time monitoring and autoscaling.

Based on a large sample of directors, I find that directors’ external connections are positively associated with the level of their compensation, suggesting that directors are compensated for the value they bring to the firm through their external connections. This association holds after controlling for the various factors such as firm and CEO characteristics, board related governance characteristics, directors’ attributes such as experience and education, industry and year fixed effects, used in prior studies. I also find that the association between the components of external connections and compensation is different for different types of directors. Director Compensation

In order to establish a smart farm, many kinds of equipment are built and operated inside and outside of a pig house. Thus, the environment for livestock (limited to pigs in this paper) in the barn is properly maintained for its growth conditions. However, due to poor environments such as closed pig houses, lack of stable power supply, inexperienced livestock management, and power outages, the failure of these environment equipment is high. Thus, there are difficulties in detecting its malfunctions during equipment operation. In this paper, based on deep learning, we provide a mechanism to quickly detect anomalies of multiple equipment (environmental sensors and controllers, etc.) in each pig house at the same time. In particular, environmental factors (temperature, humidity, CO2, ventilation, radiator temperature, external temperature, etc.) to be used for learning were extracted through the analysis of data accumulated for the generation of predictive models of each equipment. In addition, the optimal recurrent neural network (RNN) environment was derived by analyzing the characteristics of the learning RNN. In this way, the accuracy of the prediction model can be improved. In this paper, the real-time input data (only in the case of temperature) was intentionally induced above the threshold, and 93% of the abnormalities were detected to determine whether the equipment was abnormal.

The paper examines whether and how directors ' external connections affect the operating performance of the firm for which they are board members. Using a large sample of directors and senior level executives, I map the social network of directors and executives, and construct a measure of directors ' external connections capturing how connected an individual director is to directors and executives of other firms. I find a positive association between the extent of directors ' external connections and firm performance. In addition, I find that firms experience efficiency gains through cost reductions in the presence of wellconnected directors. Overall, my findings suggest that directors ' external connections provide economic benefits to firms by increasing sales growth, lowering production costs, and improving firm profitability.

The two essays in this dissertation study issues related to the board of directors’ external connections, i.e., social ties to directors and executives of other firms. The first essay examines whether and how directors’ external connections affect the operating performance of the firm for which they are board members. Using a large sample of 393,481 directors and executives from 7,627 companies over the time period from 2000 to 2010, I map the social network of directors and senior level executives, and construct a measure of directors’ external connections capturing how connected an individual director is to directors and executives of other firms. I find a positive association between the extent of directors’ external connections and firm performance. In addition, I find that firms experience efficiency gains through cost reductions in the presence of well-connected directors. Overall, my findings suggest that directors’ external connections provide economic benefits to firms by increasing sales growth, lowering production costs, and improving firm profitability. The second essay investigates whether directors’ external connections influence director compensation and appointment. Drawing on my findings in the first essay, I argue that the external connections of directors are reflected in labor market outcomes for directors. Consistent with prior literature, I find that directors’ external connections are positively associated with the level of their compensation and that directors with more extensive external connections earn more than those with more limited external connections. I then examine the impact of directors’ external connections on director appointments. Using the subsample of directors who departed as a director of one firm during the period from 2003 to 2010, I examine whether directors’ external connections impact the likelihood of being selected as a new director at another firm. I find that the extent of a director’s external connections increases the likelihood that he/she will obtain a new directorship at another firm, suggesting that external connections play an important role in director selection process. Collectively, this dissertation provides empirical evidence that relational capital captured in directors’ external connections provides access to valuable resources and information to the firm, thereby enhancing its financial performance, and has significant impact on labor market outcomes for directors in the form of director compensation and director appointment.

A self-seeded X-ray free-electron laser (XFEL) is a promising approach to realize bright, fully coherent free-electron laser (FEL) sources in the hard X-ray domain that have been a long-standing issue with longitudinal coherence remaining challenging. At the Pohang Accelerator Laboratory XFEL, we have demonstrated a hard X-ray self-seeded XFEL with a peak brightness of 3.2 × 1035 photons s–1 mm–2 mrad–2 0.1% bandwidth (BW)–1 at 9.7 keV. The bandwidth (0.19 eV) is about 1/70 times as wide (close to the Fourier transform limit) and the peak spectral brightness is 40 times higher than in self-amplified spontaneous emission (SASE), with substantial improvements in the stability of self-seeding and noticeably suppressed pedestal effects. We could reach an excellent self-seeding performance at a photon energy of 3.5 keV (lowest) and 14.6 keV (highest) with the same stability as the 9.7 keV self-seeding. The bandwidth of the 14.6 keV seeded FEL was 0.32 eV, and the peak brightness was 1.3 × 1035 photons s–1 mm–2 mrad–2 0.1%BW–1. We show that the use of seeded FEL pulses with higher reproducibility and a cleaner spectrum results in serial femtosecond crystallography data of superior quality compared with data collected using SASE mode.A hard X-ray self-seeded X-ray free-electron laser at the Pohang Accelerator Laboratory provides X-ray pulses with peak brightness of 3.2 × 1035 photons s–1 mm–2 mrad–2 0.1%BW–1 at 9.7 keV and a very small shot-to-shot electron energy jitter of 0.012%.

Thus far, attempts to develop drugs that target corticotropin-releasing hormone receptor 1 (CRF 1 R), a drug target in stress-related therapy, have been unsuccessful. Studies have focused on using high-resolution G protein-coupled receptor (GPCR) structures to develop drugs. X-ray free-electron lasers (XFELs), which prevent radiation damage and provide access to high-resolution compositions, have helped accelerate GPCR structural studies. We elucidated the crystal structure of CRF 1 R complexed with a BMK-I-152 antagonist at 2.75 Å using fixed-target serial femtosecond crystallography. The results revealed that two unique hydrogen bonds are present in the hydrogen bond network, the stalk region forms an alpha helix and the hydrophobic network contains an antagonist binding site. We then developed two antagonists—BMK-C203 and BMK-C205—and determined the CRF 1 R/BMK-C203 and CRF 1 R/BMK-C205 complex structures at 2.6 and 2.2 Å, respectively. BMK-C205 exerted significant antidepressant effects in mice and, thus, may be utilized to effectively identify structure-based drugs against CRF 1 R. Drug discovery: Hormone receptor structure helps search for new antidepressants Structural studies of complexes of a receptor protein for corticotropin-releasing hormone and specific small molecules could guide the development of new antidepressants and drugs for stress-related diseases. The activity of the corticotropin-releasing hormone receptor 1 protein has been associated with many human diseases, including psychiatric disorders, diabetes, cancer, osteoporosis, cardiovascular conditions and neurodegeneration. Hoyoung Kim at Yonsei University in Seoul, South Korea, utilized X-ray free electron laser technology, a method that minimizes sample damage, to determine the protein complexes. Through their study, they unveiled subtle, previously overlooked structural characteristics at two crucial sites. Leveraging these insights, they developed multiple ‘antagonist’ compounds that disrupt the protein’s normal function. One of these antagonists demonstrated significant antidepressant effects in mouse models, sparking hope for potential similar benefits in humans.